paper.bib

@article{Avants2009-ox,
  title = {Advanced Normalization Tools ({{ANTS}})},
  author = {{Avants} and {Tustison} and {Song}},
  year = {2009},
  journal = {Insight J.},
  doi={10.54294/uvnhin}
}

@misc{Balbastre2022-dm,
  title = {Correcting Inter-scan Motion Artifacts in Quantitative {{{\emph{R}}}} {$_1$} Mapping at {{7T}}},
  author = {Balbastre, Ya{\"e}l and Aghaeifar, Ali and Corbin, Nad{\`e}ge and Brudfors, Mikael and Ashburner, John and Callaghan, Martina F},
  year = {2022},
  volume = {88},
  number = {1},
  pages = {280--291},
  doi = {10.1002/mrm.29216},
  journal = {Magn. Reson. Med.}

}

@article{Bautin2021-dt,
  title = {Minimum Detectable Spinal Cord Atrophy with Automatic Segmentation: {{Investigations}} Using an Open-Access Dataset of Healthy Participants},
  author = {Bautin, Paul and {Cohen-Adad}, Julien},
  year = {2021},
  month = oct,
  journal = {NeuroImage. Clinical},
  volume = {32},
  pages = {102849},
  abstract = {Spinal cord atrophy is a well-known biomarker in multiple sclerosis (MS) and other diseases. It is measured by segmenting the spinal cord on an MRI image and computing the average cross-sectional area (CSA) over a few slices. Introduced about 25 years ago, this procedure is highly sensitive to the quality of the segmentation and is prone to rater-bias. Recently, fully-automated spinal cord segmentation methods, which remove the rater-bias and enable the automated analysis of large populations, have been introduced. A lingering question related to these automated methods is: How reliable are they at detecting atrophy? In this study, we evaluated the precision and accuracy of automated atrophy measurements by simulating scan-rescan experiments. Spinal cord MRI data from the open-access spine-generic project were used. The dataset aggregates 42 sites worldwide and consists of 260 healthy subjects and includes T1w and T2w contrasts. To simulate atrophy, each volume was globally rescaled at various scaling factors. Moreover, to simulate patient repositioning, random rigid transformations were applied. Using the DeepSeg algorithm from the Spinal Cord Toolbox, the spinal cord was segmented and vertebral levels were identified. Then, the average CSA between C3-C5 vertebral levels was computed for each Monte Carlo sample, allowing us to derive measures of atrophy, intra/inter-subject variability, and sample-size calculations. The minimum sample size required to detect an atrophy of 2\% between unpaired study arms, commonly seen in MS studies, was 467 +/- 13.9 using T1w and 467 +/- 3.2 using T2w images. The minimum sample size to detect a longitudinal atrophy (between paired study arms) of 0.8\% was 60 +/- 25.1 using T1w and 10 +/- 1.2 using T2w images. At the intra-subject level, the estimated CSA, observed in this study, showed good precision compared to other studies with COVs (across Monte Carlo transformations) of 0.8\% for T1w and 0.6\% for T2w images. While these sample sizes seem small, we would like to stress that these results correspond to a ``best case'' scenario, in that the dataset used here was of particularly good quality and the model for simulating atrophy does not encompass all the variability met in real-life datasets. The simulated atrophy and scan-rescan variability may over-simplify the biological reality. The proposed framework is open-source and available at https://csa-atrophy.readthedocs.io/.},
  langid = {english},
  doi = {10.1016/j.nicl.2021.102849}
}

@article{Beg2021-ps,
  title={Using Jupyter for reproducible scientific workflows},
  author={Beg, Marijan and Taka, Juliette and Kluyver, Thomas and Konovalov, Alexander and Ragan-Kelley, Min and Thi{\'e}ry, Nicolas M and Fangohr, Hans},
  journal={Computing in Science \& Engineering},
  volume={23},
  number={2},
  pages={36--46},
  year={2021},
  publisher={IEEE},
  doi = {10.1109/MCSE.2021.3052101}
}

@inproceedings{Boyle2020-ih,
  title = {The {{Courtois}} Project on Neuronal Modelling - 2020 Data Release},
  booktitle = {Annual Meeting of the Organization for Human Brain Mapping},
  author = {Boyle, Julie A and Pinsard, Basile and Boukhdhir, Amal and Belleville, Sylvie and Brambatti, Simona and Chen, Jeni and {Cohen-Adad}, Julien and Cyr, Andre and Fuente, Adrien and Rainville, Pierre and Bellec, Pierre},
  year = {2020},
  pages = {1939},
  address = {{Held virtually}}
}

@inproceedings{Boyle2020-vv,
  title = {The {{Courtois}} Project on Neuronal Modelling - 2020 Data Release},
  booktitle = {Annual Meeting of the Organization for Human Brain Mapping},
  author = {Boyle, Julie A and Pinsard, Basile and Boukhdhir, Amal and Belleville, Sylvie and Brambatti, Simona and Chen, Jeni and {Cohen-Adad}, Julien and Cyr, Andre and Fuente, Adrien and Rainville, Pierre and Bellec, Pierre},
  year = {2020},
  pages = {1939},
  address = {{Held virtually}}
}

@inproceedings{Boyle2020-wy,
  title = {Courtois Neuromod},
  booktitle = {Annual Meeting of the Organization for Human Brain Mapping},
  author = {Boyle, Julie A and Pinsard, Basile and Bellec, Pierre},
  year = {2020}
}

@inproceedings{Boyle2020-zq,
  title = {The {{Courtois}} Project on Neuronal Modelling - First Data Release},
  booktitle = {Annual {{Meeting}} of the {{Organization}} for {{Human Brain Mapping}}. {{Virtual Conference}}},
  author = {Boyle, Julie A and Pinsard, Basile and Boukhdhir, Amal and Belleville, Sylvie and Brambatti, Simona and Chen, Jeni and {Cohen-Adad}, Julien and Cyr, Andre and Fuente, Adrian and Rainville, Pierre and Bellec, Pierre},
  year = {2020},
  pages = {1939}
}

@article{Cabana2015-zg,
  title = {Quantitative Magnetization Transfer Imaging{{{\emph{made}}}}easy with {{{\emph{qMTLab}}}}: {{Software}} for Data Simulation, Analysis, and Visualization},
  author = {Cabana, Jean-Fran{\c c}ois and Gu, Ye and Boudreau, Mathieu and Levesque, Ives R and Atchia, Yaaseen and Sled, John G and Narayanan, Sridar and Arnold, Douglas L and Pike, G Bruce and {Cohen-Adad}, Julien and Duval, Tanguy and Vuong, Manh-Tung and Stikov, Nikola},
  year = {2015},
  month = sep,
  journal = {Concepts Magn. Reson. Part A Bridg. Educ. Res.},
  volume = {44A},
  number = {5},
  pages = {263--277},
  publisher = {{Wiley}},
  doi = {10.1002/cmr.a.21357},
  langid = {english}
}

@article{Catani2008-jw,
  title = {A Diffusion Tensor Imaging Tractography Atlas for Virtual in Vivo Dissections},
  author = {Catani, Marco and {Thiebaut de Schotten}, Michel},
  year = {2008},
  month = sep,
  journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
  volume = {44},
  number = {8},
  pages = {1105--1132},
  doi = {10.1016/j.cortex.2008.05.004},
  abstract = {Diffusion tensor imaging (DTI) tractography allows perform virtual dissections of white matter pathways in the living human brain. In 2002, Catani et al. published a method to reconstruct white matter pathways using a region of interest (ROI) approach. The method produced virtual representations of white matter tracts faithful to classical post-mortem descriptions but it required detailed a priori anatomical knowledge. Here, using the same approach, we provide a template to guide the delineation of ROIs for the reconstruction of the association, projection and commissural pathways of the living human brain. The template can be used for single case studies and case-control comparisons. An atlas of the 3D reconstructions of the single tracts is also provided as anatomical reference in the Montreal Neurological Institute (MNI) space.},
  langid = {english}
}

@book{Cercignani2018-ga,
  title = {Quantitative {{MRI}} of the Brain: {{Principles}} of Physical Measurement, Second Edition},
  author = {Cercignani, Mara and Dowell, Nicholas G and Tofts, Paul S},
  year = {2018},
  month = jan,
  publisher = {{CRC Press}},
  abstract = {Building on the success of the first edition of this book, the winner of the 2004 British Medical Association Radiology Medical Book Competition, Quantitative MRI of the Brain: Principles of Physical Measurement gives a unique view on how to use an MRI machine in a new way. Used as a scientific instrument it can make measurements of a myriad of physical and biological quantities in the human brain and body. For each small tissue voxel, non-invasive information monitors how tissue changes with disease and responds to treatment. The book opens with a detailed exposition of the principles of good practice in quantification, including fundamental concepts, quality assurance, MR data collection and analysis and improved study statistical power through minimised instrumental variation. There follow chapters on 14 specific groups of quantities: proton density, T1, T2, T2*, diffusion, advanced diffusion, magnetisation transfer, CEST, 1H and multi-nuclear spectroscopy, DCE-MRI, quantitative fMRI, arterial spin-labelling and image analysis, and finally a chapter on the future of quantification. The physical principles behind each quantity are stated, followed by its biological significance. Practical techniques for measurement are given, along with pitfalls and examples of clinical applications. This second edition of this indispensable 'how to' manual of quantitative MR shows the MRI physicist and research clinician how to implement these techniques on an MRI scanner to understand more about the biological processes in the patient and physiological changes in healthy controls. Although focussed on the brain, most techniques are applicable to characterising tissue in the whole body. This book is essential reading for anyone who wants to use the gamut of modern quantitative MRI methods to measure the effects of disease, its progression, and its response to treatment. Features: The first edition was awarded the book prize for Radiology by the British Medical Association in 2004 Written by an authority in the field: Professor Tofts has an international reputation for quantification in MRI Gives specific `how to' information for implementation of MRI measurement sequence techniques},
  langid = {english}
}

@article{Chung2010-nc,
  title = {Rapid {{B1}}+ Mapping Using a Preconditioning {{RF}} Pulse with {{TurboFLASH}} Readout},
  author = {Chung, Sohae and Kim, Daniel and Breton, Elodie and Axel, Leon},
  year = {2010},
  month = aug,
  journal = {Magnetic Resonance in Medicine},
  volume = {64},
  number = {2},
  pages = {439--446},
  doi = {10.1002/mrm.22423},
  abstract = {In MRI, the transmit radiofrequency field (B(1)(+)) inhomogeneity can lead to signal intensity variations and quantitative measurement errors. By independently mapping the local B(1)(+) variation, the radiofrequency-related signal variations can be corrected for. In this study, we present a new fast B(1)(+) mapping method using a slice-selective preconditioning radiofrequency pulse. Immediately after applying a slice-selective preconditioning pulse, a turbo fast low-angle-shot imaging sequence with centric k-space reordering is performed to capture the residual longitudinal magnetization left behind by the slice-selective preconditioning pulse due to B(1)(+) variation. Compared to the reference double-angle method, this method is considerably faster. Specifically, the total scan time for the double-angle method is equal to the product of 2 (number of images), the number of phase-encoding lines, and approximately 5T(1), whereas the slice-selective preconditioning method takes approximately 5T(1). This method was validated in vitro and in vivo with a 3-T whole-body MRI system. The combined brain and pelvis B(1)(+) measurements showed excellent agreement and strong correlation with those by the double-angle method (mean difference = 0.025; upper and lower 95\% limits of agreement were -0.07 and 0.12; R = 0.93; P \textexclamdown{} 0.001). This fast B(1)(+) mapping method can be used for a variety of applications, including body imaging where fast imaging is desirable.},
  langid = {english}
}

@misc{Cohen-Adad2020-qz,
  title = {Spine Generic Public Database (Single Subject)},
  author = {{Cohen-Adad}, Julien},
  year = {2020},
  month = nov
}

@article{Cohen-Adad2021-qo,
  title = {Generic Acquisition Protocol for Quantitative {{MRI}} of the Spinal Cord},
  author = {{Cohen-Adad}, Julien and {Alonso-Ortiz}, Eva and Abramovic, Mihael and Arneitz, Carina and Atcheson, Nicole and Barlow, Laura and Barry, Robert L and Barth, Markus and Battiston, Marco and B{\"u}chel, Christian and others},
  year = {2021},
  journal = {Nature protocols},
  volume = {16},
  number = {10},
  pages = {4611--4632},
  publisher = {{Nature Publishing Group UK London}},
  doi = {10.1038/s41596-021-00588-0}
}

@article{Cohen-Adad2021-so,
  title = {Open-Access Quantitative {{MRI}} Data of the Spinal Cord and Reproducibility across Participants, Sites and Manufacturers},
  author = {{Cohen-Adad}, Julien and {Alonso-Ortiz}, Eva and Abramovic, Mihael and Arneitz, Carina and Atcheson, Nicole and Barlow, Laura and Barry, Robert L and Barth, Markus and Battiston, Marco and B{\"u}chel, Christian and others},
  year = {2021},
  journal = {Scientific data},
  volume = {8},
  number = {1},
  pages = {219},
  publisher = {{Nature Publishing Group UK London}},
  doi = {10.1038/s41597-021-01044-0}
}

@article{Cooper2020-bl,
  title = {Quantitative {{Multi-Parameter}} Mapping Optimized for the Clinical Routine},
  author = {Cooper, Graham and Hirsch, Sebastian and Scheel, Michael and Brandt, Alexander U and Paul, Friedemann and Finke, Carsten and {Boehm-Sturm}, Philipp and Hetzer, Stefan},
  year = {2020},
  month = dec,
  journal = {Serotonin Receptors in Neurobiology},
  volume = {14},
  pages = {611194},
  doi = {10.3389/fnins.2020.611194},
  abstract = {Using quantitative multi-parameter mapping (MPM), studies can investigate clinically relevant microstructural changes with high reliability over time and across subjects and sites. However, long acquisition times (20 min for the standard 1-mm isotropic protocol) limit its translational potential. This study aimed to evaluate the sensitivity gain of a fast 1.6-mm isotropic MPM protocol including post-processing optimized for longitudinal clinical studies. 6 healthy volunteers (35{$\pm$}7 years old; 3 female) were scanned at 3T to acquire the following whole-brain MPM maps with 1.6 mm isotropic resolution: proton density (PD), magnetization transfer saturation (MT), longitudinal relaxation rate (R1), and transverse relaxation rate (R2*). MPM maps were generated using two RF transmit field (B1+) correction methods: (1) using an acquired B1+ map and (2) using a data-driven approach. Maps were generated with and without Gibb's ringing correction. The intra-/inter-subject coefficient of variation (CoV) of all maps in the gray and white matter, as well as in all anatomical regions of a fine-grained brain atlas, were compared between the different post-processing methods using Student's t-test. The intra-subject stability of the 1.6-mm MPM protocol is 2-3 times higher than for the standard 1-mm sequence and can be achieved in less than half the scan duration. Intra-subject variability for all four maps in white matter ranged from 1.2-5.3\% and in gray matter from 1.8 to 9.2\%. Bias-field correction using an acquired B1+ map significantly improved intra-subject variability of PD and R1 in the gray (42\%) and white matter (54\%) and correcting the raw images for the effect of Gibb's ringing further improved intra-subject variability in all maps in the gray (11\%) and white matter (10\%). Combining Gibb's ringing correction and bias field correction using acquired B1+ maps provides excellent stability of the 7-min MPM sequence with 1.6 mm resolution suitable for the clinical routine.},
  langid = {english}
}

@article{Cordes2020-vz,
  title = {Portable and Platform-Independent {{MR}} Pulse Sequence Programs},
  author = {Cordes, Cristoffer and Konstandin, Simon and Porter, David and G{\"u}nther, Matthias},
  year = {2020},
  month = apr,
  journal = {Magnetic Resonance in Medicine},
  volume = {83},
  number = {4},
  pages = {1277--1290},
  publisher = {{Wiley}},
  doi = {10.1002/mrm.28020},
  abstract = {PURPOSE: To introduce a new sequence description format for vendor-independent MR sequences that include all calculation logic portably. To introduce a new MRI sequence development approach which utilizes flexibly reusable modules. METHODS: The proposed sequence description contains a sequence module hierarchy for loop and group logic, which is enhanced by a novel strategy for performing efficient parameter and pulse shape calculation. These calculations are powered by a flow graph structure. By using the flow graph, all calculations are performed with no redundancy and without requiring preprocessing. The generation of this interpretable structure is a separate step that combines MRI techniques while actively considering their context. The driver interface is slim and highly flexible through scripting support. The sequences do not require any vendor-specific compiling or processing step. A vendor-independent frontend for sequence configuration can be used. Tests that ensure physical feasibility of the sequence are integrated into the calculation logic. RESULTS: The framework was used to define a set of standard sequences. Resulting images were compared to respective images acquired with sequences provided by the device manufacturer. Images were acquired using a standard commercial MRI system. CONCLUSIONS: The approach produces configurable, vendor-independent sequences, whose configurability enables rapid prototyping. The transparent data structure simplifies the process of sharing reproducible sequences, modules, and techniques.},
  langid = {english},
  keywords = {high performance computing,modular MR sequence development,platform-independent pulse sequence programming,portable,reproducible,vendor-independent MRI}
}

@article{Cousineau2017-ce,
  title = {A Test-Retest Study on {{Parkinson}}'s {{PPMI}} Dataset Yields Statistically Significant White Matter Fascicles},
  author = {Cousineau, Martin and Jodoin, Pierre-Marc and Garyfallidis, Eleftherios and C{\^o}t{\'e}, Marc-Alexandre and Morency, F{\'e}lix C and Rozanski, Verena and Grand'Maison, Marilyn and Bedell, Barry J and Descoteaux, Maxime},
  year = {2017},
  month = jan,
  journal = {NeuroImage: Clinical},
  volume = {16},
  pages = {222--233},
  doi = {10.1016/j.nicl.2017.07.020},
  abstract = {In this work, we propose a diffusion MRI protocol for mining Parkinson's disease diffusion MRI datasets and recover robust disease-specific biomarkers. Using advanced high angular resolution diffusion imaging (HARDI) crossing fiber modeling and tractography robust to partial volume effects, we automatically dissected 50 white matter (WM) fascicles. These fascicles connect deep nuclei (thalamus, putamen, pallidum) to different cortical functional areas (associative, motor, sensorimotor, limbic), basal forebrain and substantia nigra. Then, among these 50 candidate WM fascicles, only the ones that passed a test-retest reproducibility procedure qualified for further tractometry analysis. Leveraging the unique 2-timepoints test-retest Parkinson's Progression Markers Initiative (PPMI) dataset of over 600 subjects, we found statistically significant differences in tract profiles along the subcortico-cortical pathways between Parkinson's disease patients and healthy controls. In particular, significant increases in FA, apparent fiber density, tract-density and generalized FA were detected in some locations of the nigro-subthalamo-putaminal-thalamo-cortical pathway. This connection is one of the major motor circuits balancing the coordination of motor output. Detailed and quantifiable knowledge on WM fascicles in these areas is thus essential to improve the quality and outcome of Deep Brain Stimulation, and to target new WM locations for investigation.},
  keywords = {Diffusion,MRI,Parkinson,Test-retest,Tractography,Tractometry,White matter}
}

@article{Davis1998-lr,
  title = {Calibrated Functional {{MRI}}: {{Mapping}} the Dynamics of Oxidative Metabolism},
  author = {Davis, Timothy L and Kwong, Kenneth K and Weisskoff, Robert M and Rosen, Bruce R},
  year = {1998},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {95},
  number = {4},
  pages = {1834--1839},
  doi = {10.1073/pnas.95.4.1834},
  abstract = {MRI was extended to the measurement of changes in oxidative metabolism in the normal human during functionally induced changes in cellular activity. A noninvasive MRI method that is model-independent calibrates the blood oxygen level dependent (BOLD) signal of functional MRI (fMRI) against perfusion-sensitive MRI, using carbon dioxide breathing as a physiological reference standard. This calibration procedure provides a regional measurement of the expected sensitivity of the fMRI BOLD signal to changes in the cellular activity of the brain. Maps of the BOLD signal calibration factor showed regional heterogeneity, indicating that the magnitude of functionally induced changes in the BOLD signal will be dependent on both the local change in blood flow and the local baseline physiology of the cerebral cortex. BOLD signal magnitude is shown to be reduced by 32\% from its expected level by the action of oxygen metabolism. The calibrated fMRI technique was applied to stimulation of the human visual cortex with an alternating radial checkerboard pattern. With this stimulus oxygen consumption increased 16\% whereas blood flow increased 45\%. Although this result is consistent with previous findings of a significant difference between the increase in blood flow and oxygen consumption, it does indicate clearly that oxidative metabolism is a significant component of the metabolic response of the brain to functionally induced changes in cellular activity.}
}

@article{DeLeener2017-yq,
  title = {{{SCT}}: {{Spinal Cord Toolbox}}, an Open-Source Software for Processing Spinal Cord {{MRI}} Data},
  author = {De Leener, Benjamin and L{\'e}vy, Simon and Dupont, Sara M and Fonov, Vladimir S and Stikov, Nikola and Louis Collins, D and Callot, Virginie and {Cohen-Adad}, Julien},
  year = {2017},
  month = jan,
  journal = {Neuroimage},
  volume = {145},
  pages = {24--43},
  doi = {10.1016/j.neuroimage.2016.10.009},
  abstract = {For the past 25 years, the field of neuroimaging has witnessed the development of several software packages for processing multi-parametric magnetic resonance imaging (mpMRI) to study the brain. These software packages are now routinely used by researchers and clinicians, and have contributed to important breakthroughs for the understanding of brain anatomy and function. However, no software package exists to process mpMRI data of the spinal cord. Despite the numerous clinical needs for such advanced mpMRI protocols (multiple sclerosis, spinal cord injury, cervical spondylotic myelopathy, etc.), researchers have been developing specific tools that, while necessary, do not provide an integrative framework that is compatible with most usages and that is capable of reaching the community at large. This hinders cross-validation and the possibility to perform multi-center studies. In this study we introduce the Spinal Cord Toolbox (SCT), a comprehensive software dedicated to the processing of spinal cord MRI data. SCT builds on previously-validated methods and includes state-of-the-art MRI templates and atlases of the spinal cord, algorithms to segment and register new data to the templates, and motion correction methods for diffusion and functional time series. SCT is tailored towards standardization and automation of the processing pipeline, versatility, modularity, and it follows guidelines of software development and distribution. Preliminary applications of SCT cover a variety of studies, from cross-sectional area measures in large databases of patients, to the precise quantification of mpMRI metrics in specific spinal pathways. We anticipate that SCT will bring together the spinal cord neuroimaging community by establishing standard templates and analysis procedures.},
  keywords = {Atlas,MRI,Open-source,Software,Spinal cord,Template}
}

@article{DeLeener2018-yb,
  title = {{{PAM50}}: {{Unbiased}} Multimodal Template of the Brainstem and Spinal Cord Aligned with the {{ICBM152}} Space},
  author = {De Leener, Benjamin and Fonov, Vladimir S and Collins, D Louis and Callot, Virginie and Stikov, Nikola and {Cohen-Adad}, Julien},
  year = {2018},
  month = jan,
  journal = {Neuroimage},
  volume = {165},
  pages = {170--179},
  doi = {10.1016/j.neuroimage.2017.10.041},
  abstract = {Template-based analysis of multi-parametric MRI data of the spinal cord sets the foundation for standardization and reproducibility, thereby helping the discovery of new biomarkers of spinal-related diseases. While MRI templates of the spinal cord have been recently introduced, none of them cover the entire spinal cord. In this study, we introduced an unbiased multimodal MRI template of the spinal cord and the brainstem, called PAM50, which is anatomically compatible with the ICBM152 brain template and uses the same coordinate system. The PAM50 template is based on 50 healthy subjects, covers the full spinal cord (C1 to L2 vertebral levels) and the brainstem, is available for T1-, T2-and T2*-weighted MRI contrasts and includes a probabilistic atlas of the gray matter and white matter tracts. Template creation accuracy was assessed by computing the mean and maximum distance error between each individual spinal cord centerline and the PAM50 centerline, after registration to the template. Results showed high accuracy for both T1- (mean = 0.37 {$\pm$} 0.06 mm; max = 1.39 {$\pm$} 0.58 mm) and T2-weighted (mean = 0.11 {$\pm$} 0.03 mm; max = 0.71 {$\pm$} 0.27 mm) contrasts. Additionally, the preservation of the spinal cord topology during the template creation process was verified by comparing the cross-sectional area (CSA) profile, averaged over all subjects, and the CSA profile of the PAM50 template. The fusion of the PAM50 and ICBM152 templates will facilitate group and multi-center studies of combined brain and spinal cord MRI, and enable the use of existing atlases of the brainstem compatible with the ICBM space.},
  langid = {english},
  keywords = {Atlas,ICBM,MRI,Spinal cord,Template}
}

@article{Desikan2006-jc,
  title = {An Automated Labeling System for Subdividing the Human Cerebral Cortex on {{MRI}} Scans into Gyral Based Regions of Interest},
  author = {Desikan, Rahul S and S{\'e}gonne, Florent and Fischl, Bruce and Quinn, Brian T and Dickerson, Bradford C and Blacker, Deborah and Buckner, Randy L and Dale, Anders M and Maguire, R Paul and Hyman, Bradley T and Albert, Marilyn S and Killiany, Ronald J},
  year = {2006},
  month = jul,
  journal = {Neuroimage},
  volume = {31},
  number = {3},
  pages = {968--980},
  doi = {10.1016/j.neuroimage.2006.01.021},
  abstract = {In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.},
  langid = {english}
}

@article{DiTommaso2017-qk,
  title = {Nextflow Enables Reproducible Computational Workflows},
  author = {Di Tommaso, Paolo and Chatzou, Maria and Floden, Evan W and Barja, Pablo Prieto and Palumbo, Emilio and Notredame, Cedric},
  year = {2017},
  month = apr,
  journal = {Nature Biotechnology},
  volume = {35},
  number = {4},
  pages = {316--319},
  publisher = {{Nature Publishing Group}},
  doi = {10.1038/nbt.3820},
  langid = {english}
}

@article{Duchesne2019-ls,
  title = {Structural and Functional Multi-Platform {{MRI}} Series of a Single Human Volunteer over More than Fifteen Years},
  author = {Duchesne, Simon and Dieumegarde, Louis and Chouinard, Isabelle and Farokhian, Farnaz and Badhwar, Amanpreet and Bellec, Pierre and T{\'e}treault, Pascal and Descoteaux, Maxime and Bor{\'e}, Arnaud and Houde, Jean-Christophe and Beaulieu, Christian and Potvin, Olivier},
  year = {2019},
  month = oct,
  journal = {Scientific Data},
  volume = {6},
  number = {1},
  pages = {1--9},
  publisher = {{Nature Publishing Group}},
  abstract = {We present MRI data from a single human volunteer consisting in over 599 multi-contrast MR images (T1-weighted, T2-weighted, proton density, fluid-attenuated inversion recovery, T2* gradient-echo, diffusion, susceptibility-weighted, arterial-spin labelled, and resting state BOLD functional connectivity imaging) acquired in over 73 sessions on 36 different scanners (13 models, three manufacturers) over the course of 15+ years (cf. Data records). Data included planned data collection acquired within the Consortium pour l'identification pr\'ecoce de la maladie Alzheimer - Qu\'ebec (CIMA-Q) and Canadian Consortium on Neurodegeneration in Aging (CCNA) studies, as well as opportunistic data collection from various protocols. These multiple within- and between-centre scans over a substantial time course of a single, cognitively healthy volunteer can be useful to answer a number of methodological questions of interest to the community. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9925037},
  langid = {english},
  doi = {10.1038/s41597-019-0262-8}
}

@article{DuPre2022-uf,
  title = {Beyond Advertising: {{New}} Infrastructures for Publishing Integrated Research Objects},
  author = {DuPre, Elizabeth and Holdgraf, Chris and Karakuzu, Agah and Tetrel, Lo{\"i}c and Bellec, Pierre and Stikov, Nikola and Poline, Jean-Baptiste},
  year = {2022},
  month = jan,
  journal = {Plos Computational Biology},
  volume = {18},
  number = {1},
  pages = {e1009651},
  doi = {10.1371/journal.pcbi.1009651},
  langid = {english}
}

@article{Erramuzpe2021-rw,
  title = {A Comparison of Quantitative {{R1}} and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex},
  author = {Erramuzpe, A and Schurr, R and Yeatman, J D and Gotlib, I H and Sacchet, M D and Travis, K E and Feldman, H M and Mezer, A A},
  year = {2021},
  month = jan,
  journal = {Cerebral Cortex},
  volume = {31},
  number = {2},
  pages = {1211--1226},
  doi = {10.1093/cercor/bhaa288},
  abstract = {Brain development and aging are complex processes that unfold in multiple brain regions simultaneously. Recently, models of brain age prediction have aroused great interest, as these models can potentially help to understand neurological diseases and elucidate basic neurobiological mechanisms. We test whether quantitative magnetic resonance imaging can contribute to such age prediction models. Using R1, the longitudinal rate of relaxation, we explore lifespan dynamics in cortical gray matter. We compare R1 with cortical thickness, a well-established biomarker of brain development and aging. Using 160 healthy individuals (6-81 years old), we found that R1 and cortical thickness predicted age similarly, but the regions contributing to the prediction differed. Next, we characterized R1 development and aging dynamics. Compared with anterior regions, in posterior regions we found an earlier R1 peak but a steeper postpeak decline. We replicate these findings: firstly, we tested a subset (N = 10) of the original dataset for whom we had additional scans at a lower resolution; and second, we verified the results on an independent dataset (N = 34). Finally, we compared the age prediction models on a subset of 10 patients with multiple sclerosis. The patients are predicted older than their chronological age using R1 but not with cortical thickness.},
  langid = {english},
  keywords = {aging,cortex,multiple sclerosis,prediction,qMRI}
}

@misc{Esteban2022-pi,
  title = {{{sMRIPrep}}: {{Structural MRI PREProcessing}} Workflows},
  author = {Esteban, Oscar and Markiewicz, Christopher J and Blair, Ross and Poldrack, Russell A and Gorgolewski, Krzysztof J},
  year = {2022},
  month = nov
}

@article{Garyfallidis2014-gu,
  title = {Dipy, a Library for the Analysis of Diffusion {{MRI}} Data},
  author = {Garyfallidis, Eleftherios and Brett, Matthew and Amirbekian, Bagrat and Rokem, Ariel and {van der Walt}, Stefan and Descoteaux, Maxime and {Nimmo-Smith}, Ian and {Dipy Contributors}},
  year = {2014},
  doi={10.3389/fninf.2014.00008},
  month = feb,
  volume = {8},
  pages = {8},
  journal={Frontiers in Neuroinformatics}, 
  abstract = {Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.},
  langid = {english},
  keywords = {diffusion MRI,dMRI,DSI,DTI,free open source software,HARDI,Python,tractography}
}

@article{Ge2002-fz,
  title = {Age-Related Total Gray Matter and White Matter Changes in Normal Adult Brain. {{Part II}}: Quantitative Magnetization Transfer Ratio Histogram Analysis},
  author = {Ge, Yulin and Grossman, Robert I and Babb, James S and Rabin, Marcie L and Mannon, Lois J and Kolson, Dennis L},
  year = {2002},
  month = sep,
  journal = {AJNR: American Journal of Neuroradiology},
  volume = {23},
  number = {8},
  pages = {1334--1341},
  abstract = {BACKGROUND AND PURPOSE: The magnetization transfer ratio (MTR) is a sensitive and quantitative identifier of underlying structural changes in the brain. We quantitatively evaluated age- and sex-related MTR changes in global gray matter (GM) and global white matter (WM) in healthy adults. METHODS: Fifty-two healthy volunteers (21 men, 31 women) aged 20-86 years underwent dual-echo fast spin-echo and magnetization transfer imaging performed with and then without a saturation pulse. GM and WM were distinguished by using a computer-assisted semiautomated segmentation technique. MTR histograms were generated for each segmented tissue in each subject and compared among age and sex groups. RESULTS: The mean, median, first quartile, and peak height of the MTR histogram were significantly lower in the older group (\textquestiondown{} or =50 years) than those in the younger group (\textexclamdown 50 years) for both GM and WM. The age dependency of these values can be expressed in a quadratic fashion over the entire span of adulthood. The MTRs started to decline only after the age of approximately 40 years in both tissues. No statistically significant differences in MTR histogram measurements between the sexes were observed. CONCLUSION: The different MTR values for both GM and WM in the two age groups suggest that notable microscopic changes occur in GM and WM with advancing age, yet no significant sex-related variations in MTR measurements were found in these neurologically healthy adults. Such normative data based on the inherent contrast in MTRs are essential in studies of specific disorders of aging, and they may have implications for our understanding of the gross structural changes in both GM and WM in the aging brain.},
  langid = {english}
}

@article{Girard2014-gs,
  title = {Towards Quantitative Connectivity Analysis: Reducing Tractography Biases},
  author = {Girard, Gabriel and Whittingstall, Kevin and Deriche, Rachid and Descoteaux, Maxime},
  year = {2014},
  month = sep,
  journal = {Neuroimage},
  volume = {98},
  pages = {266--278},
  doi = {10.1016/j.neuroimage.2014.04.074},
  abstract = {Diffusion MRI tractography is often used to estimate structural connections between brain areas and there is a fast-growing interest in quantifying these connections based on their position, shape, size and length. However, a portion of the connections reconstructed with tractography is biased by their position, shape, size and length. Thus, connections reconstructed are not equally distributed in all white matter bundles. Quantitative measures of connectivity based on the streamline distribution in the brain such as streamline count (density), average length and spatial extent (volume) are biased by erroneous streamlines produced by tractography algorithms. In this paper, solutions are proposed to reduce biases in the streamline distribution. First, we propose to optimize tractography parameters in terms of connectivity. Then, we propose to relax the tractography stopping criterion with a novel probabilistic stopping criterion and a particle filtering method, both based on tissue partial volume estimation maps calculated from a T1-weighted image. We show that optimizing tractography parameters, stopping and seeding strategies can reduce the biases in position, shape, size and length of the streamline distribution. These tractography biases are quantitatively reported using in-vivo and synthetic data. This is a critical step towards producing tractography results for quantitative structural connectivity analysis.},
  langid = {english},
  keywords = {anatomical MRI,connectivity analysis,diffusion MRI,particle filtering,white matter tractography}
}

@misc{Golay2022-zr,
  title = {Phantom for {{Multi-Parametric}} Calibration in Magnetic Resonance Imaging},
  author = {Golay, Xavier and {Oliver-Taylor}, Aaron},
  year = {2022},
  month = jul,
  number = {11,391,804 B2}
}

@article{Gorgolewski2016-xt,
  title = {The Brain Imaging Data Structure, a Format for Organizing and Describing Outputs of Neuroimaging Experiments},
  author = {Gorgolewski, Krzysztof J and Auer, Tibor and Calhoun, Vince D and Craddock, R Cameron and Das, Samir and Duff, Eugene P and Flandin, Guillaume and Ghosh, Satrajit S and Glatard, Tristan and Halchenko, Yaroslav O and Handwerker, Daniel A and Hanke, Michael and Keator, David and Li, Xiangrui and Michael, Zachary and Maumet, Camille and Nichols, B Nolan and Nichols, Thomas E and Pellman, John and Poline, Jean-Baptiste and Rokem, Ariel and Schaefer, Gunnar and Sochat, Vanessa and Triplett, William and Turner, Jessica A and Varoquaux, Ga{\"e}l and Poldrack, Russell A},
  year = {2016},
  month = jun,
  journal = {Scientific Data},
  volume = {3},
  number = {1},
  pages = {1--9},
  publisher = {{Nature Publishing Group}},
  doi = {10.1038/sdata.2016.44},
  abstract = {The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.},
  langid = {english}
}

@article{Gracien2017-un,
  title = {Evaluation of Brain Ageing: A Quantitative Longitudinal {{MRI}} Study over 7 Years},
  author = {Gracien, Ren{\'e}-Maxime and N{\"u}rnberger, Lucas and Hok, Pavel and Hof, Stephanie-Michelle and Reitz, Sarah C and R{\"u}b, Udo and Steinmetz, Helmuth and {Hilker-Roggendorf}, R{\"u}diger and Klein, Johannes C and Deichmann, Ralf and Baudrexel, Simon},
  year = {2017},
  month = apr,
  journal = {European Radiology},
  volume = {27},
  number = {4},
  pages = {1568--1576},
  doi = {10.1007/s00330-016-4485-1},
  abstract = {OBJECTIVES: T1 relaxometry is a promising tool for the assessment of microstructural changes during brain ageing. Previous cross-sectional studies demonstrated increasing T1 values in white and decreasing T1 values in grey matter over the lifetime. However, these findings have not yet been confirmed on the basis of a longitudinal study. In this longitudinal study over 7 years, T1 relaxometry was used to investigate the dynamics of age-related microstructural changes in older healthy subjects. METHODS: T1 mapping was performed in 17 healthy subjects (range 51-77 years) at baseline and after 7 years. Advanced cortical and white matter segmentation was used to determine mean T1 values in the cortex and white matter. RESULTS: The analysis revealed a decrease of mean cortical T1 values over 7 years, the rate of T1 reduction being more prominent in subjects with higher age. T1 decreases were predominantly localized in the lateral frontal, parietal and temporal cortex. In contrast, mean white matter T1 values remained stable. CONCLUSIONS: T1 mapping is shown to be sensitive to age-related microstructural changes in healthy ageing subjects in a longitudinal setting. Data of a cohort in late adulthood and the senescence period demonstrate a decrease of cortical T1 values over 7 years, most likely reflecting decreasing water content and increased iron concentrations. KEY POINTS: * T1 mapping is sensitive to age-related microstructural changes in a longitudinal setting. * T1 decreases were predominantly localized in the lateral frontal, parietal and temporal cortex. * The rate of T1 reduction was more prominent in subjects with higher age. * These changes most likely reflect decreasing cortical water and increasing iron concentrations.},
  langid = {english},
  keywords = {Ageing,Cerebral cortex,Quantitative magnetic resonance imaging,T1 relaxation,White matter}
}

@article{Gracien2020-js,
  title = {How Stable Is Quantitative {{MRI}}? - {{Assessment}} of Intra- and Inter-Scanner-Model Reproducibility Using Identical Acquisition Sequences and Data Analysis Programs},
  author = {Gracien, Ren{\'e}-Maxime and Maiworm, Michelle and Br{\"u}che, Nadine and Shrestha, Manoj and N{\"o}th, Ulrike and Hattingen, Elke and Wagner, Marlies and Deichmann, Ralf},
  year = {2020},
  month = feb,
  journal = {Neuroimage},
  doi = {10.1016/j.neuroimage.2019.116364},
  volume = {207},
  pages = {116364},
  abstract = {BACKGROUND: Quantitative MRI (qMRI) techniques allow assessing cerebral tissue properties. However, previous studies on the accuracy of quantitative T1 and T2 mapping reported a scanner model bias of up to 10\% for T1 and up to 23\% for T2. Such differences would render multi-centre qMRI studies difficult and raise fundamental questions about the general precision of qMRI. A problem in previous studies was that different methods were used for qMRI parameter mapping or for measuring the transmitted radio frequency field B1 which is critical for qMRI techniques requiring corrections for B1 non-uniformities. AIMS: The goal was to assess the intra- and inter-scanner reproducibility of qMRI data at 3 \hspace{0pt}T, using two different scanner models from the same vendor with exactly the same multiparametric acquisition protocol. METHODS: Proton density (PD), T1, T2* and T2 mapping was performed on healthy subjects and on a phantom, performing each measurement twice for each of two scanner models. Although the scanners had different hardware and software versions, identical imaging sequences were used for PD, T1 and T2* mapping, adapting the codes of an existing protocol on the older system line by line to match the software version of the newer scanner. For T2-mapping, the respective manufacturer's sequence was used which depended on the software version. However, system-dependent corrections were carried out in this case. Reproducibility was assessed by average values in regions of interest. RESULTS: Mean scan-rescan variations were not exceeding 2.14\%, with average values of 1.23\% and 1.56\% for the new and old system, respectively. Inter-scanner model deviations were not exceeding 5.21\% with average values of about 2.2-3.8\% for PD, 2.5-3.0\% for T2*, 1.6-3.1\% for T1 and 3.3-5.2\% for T2. CONCLUSIONS: Provided that identical acquisition sequences are used, discrepancies between qMRI data acquired with different scanner models are low. The level of systematic differences reported in this work may help to interpret multi-centre data.},
  langid = {english}
}

@article{Gros2019-ss,
  title = {Automatic Segmentation of the Spinal Cord and Intramedullary Multiple Sclerosis Lesions with Convolutional Neural Networks},
  author = {Gros, Charley and De Leener, Benjamin and Badji, Atef and Maranzano, Josefina and Eden, Dominique and Dupont, Sara M and Talbott, Jason and Zhuoquiong, Ren and Liu, Yaou and Granberg, Tobias and Ouellette, Russell and Tachibana, Yasuhiko and Hori, Masaaki and Kamiya, Kouhei and Chougar, Lydia and Stawiarz, Leszek and Hillert, Jan and Bannier, Elise and Kerbrat, Anne and Edan, Gilles and Labauge, Pierre and Callot, Virginie and Pelletier, Jean and Audoin, Bertrand and Rasoanandrianina, Henitsoa and Brisset, Jean-Christophe and Valsasina, Paola and Rocca, Maria A and Filippi, Massimo and Bakshi, Rohit and Tauhid, Shahamat and Prados, Ferran and Yiannakas, Marios and Kearney, Hugh and Ciccarelli, Olga and Smith, Seth and Treaba, Constantina Andrada and Mainero, Caterina and Lefeuvre, Jennifer and Reich, Daniel S and Nair, Govind and Auclair, Vincent and McLaren, Donald G and Martin, Allan R and Fehlings, Michael G and Vahdat, Shahabeddin and Khatibi, Ali and Doyon, Julien and Shepherd, Timothy and Charlson, Erik and Narayanan, Sridar and {Cohen-Adad}, Julien},
  year = {2019},
  month = jan,
  journal = {Neuroimage},
  volume = {184},
  pages = {901--915},
  doi = {10.1016/j.neuroimage.2018.09.081},
  abstract = {The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2{${_\ast}$}-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95\% vs. 88\% for PropSeg (p {$\leq$} 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60\%, a relative volume difference of -15\%, and a lesion-wise detection sensitivity and precision of 83\% and 77\%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.},
  langid = {english},
  keywords = {Convolutional neural networks,MRI,Multiple sclerosis,Segmentation,Spinal cord}
}

@article{Hagiwara2019-pg,
  title = {Linearity, Bias, Intrascanner Repeatability, and Interscanner Reproducibility of Quantitative Multidynamic Multiecho Sequence for Rapid Simultaneous Relaxometry at 3 {{T}}: {{A}} Validation Study with a Standardized Phantom and Healthy Controls},
  author = {Hagiwara, Akifumi and Hori, Masaaki and {Cohen-Adad}, Julien and Nakazawa, Misaki and Suzuki, Yuichi and Kasahara, Akihiro and Horita, Moeko and Haruyama, Takuya and Andica, Christina and Maekawa, Tomoko and Kamagata, Koji and Kumamaru, Kanako Kunishima and Abe, Osamu and Aoki, Shigeki},
  year = {2019},
  month = jan,
  journal = {Investigative Radiology},
  volume = {54},
  number = {1},
  pages = {39--47},
  doi = {10.1097/RLI.0000000000000510},
  abstract = {OBJECTIVES: The aim of this study was to evaluate the linearity, bias, intrascanner repeatability, and interscanner reproducibility of quantitative values derived from a multidynamic multiecho (MDME) sequence for rapid simultaneous relaxometry. MATERIALS AND METHODS: The NIST/ISMRM (National Institute of Standards and Technology/International Society for Magnetic Resonance in Medicine) phantom, containing spheres with standardized T1 and T2 relaxation times and proton density (PD), and 10 healthy volunteers, were scanned 10 times on different days and 2 times during the same session, using the MDME sequence, on three 3 T scanners from different vendors. For healthy volunteers, brain volumetry and myelin estimation were performed based on the measured T1, T2, and PD. The measured phantom values were compared with reference values; volunteer values were compared with their averages across 3 scanners. RESULTS: The linearity of both phantom and volunteer measurements in T1, T2, and PD values was very strong (R = 0.973-1.000, 0.979-1.000, and 0.982-0.999, respectively) The highest intrascanner coefficients of variation (CVs) for T1, T2, and PD were 2.07\%, 7.60\%, and 12.86\% for phantom data, and 1.33\%, 0.89\%, and 0.77\% for volunteer data, respectively. The highest interscanner CVs of T1, T2, and PD were 10.86\%, 15.27\%, and 9.95\% for phantom data, and 3.15\%, 5.76\%, and 3.21\% for volunteer data, respectively. Variation of T1 and T2 tended to be larger at higher values outside the range of those typically observed in brain tissue. The highest intrascanner and interscanner CVs for brain tissue volumetry were 2.50\% and 5.74\%, respectively, for cerebrospinal fluid. CONCLUSIONS: Quantitative values derived from the MDME sequence are overall robust for brain relaxometry and volumetry on 3 T scanners from different vendors. Caution is warranted when applying MDME sequence on anatomies with relaxometry values outside the range of those typically observed in brain tissue.},
  langid = {english}
}

@article{Hagiwara2021-bz,
  title = {Age-{{Related}} Changes in Relaxation Times, Proton Density, Myelin, and Tissue Volumes in Adult Brain Analyzed by 2-{{Dimensional}} Quantitative Synthetic Magnetic Resonance Imaging},
  author = {Hagiwara, Akifumi and Fujimoto, Kotaro and Kamagata, Koji and Murata, Syo and Irie, Ryusuke and Kaga, Hideyoshi and Someya, Yuki and Andica, Christina and Fujita, Shohei and Kato, Shimpei and Fukunaga, Issei and Wada, Akihiko and Hori, Masaaki and Tamura, Yoshifumi and Kawamori, Ryuzo and Watada, Hirotaka and Aoki, Shigeki},
  year = {2021},
  month = mar,
  journal = {Investigative Radiology},
  volume = {56},
  number = {3},
  pages = {163},
  doi = {10.1097/RLI.0000000000000720},
  abstract = {erformed automatically. This study aimed to reveal the changes in tissue characteristics and volumes of the brain according to age and provide age-specific reference values obtained by quantitative synthetic MRI. Materials and Methods This was a prospective study of healthy subjects with no history of brain diseases scanned with a multidynamic multiecho sequence for simultaneous measurement of relaxometry of T1, T2, and PD. We performed myelin estimation and brain volumetry based on these values. We performed volume-of-interest analysis on both gray matter (GM) and white matter (WM) regions for T1, T2, PD, and myelin volume fraction maps. Tissue volumes were calculated in the whole brain, producing brain parenchymal volume, GM volume, WM volume, and myelin volume. These volumes were normalized by intracranial volume to a brain parenchymal fraction, GM fraction, WM fraction, and myelin fraction (MyF). We examined the changes in the mean regional quantitative values and segmented tissue volumes according to age. Results We analyzed data of 114 adults (53 men and 61 women; median age, 66.5 years; range, 21\textendash 86 years). T1, T2, and PD values showed quadratic changes according to age and stayed stable or decreased until around 60 years of age and increased thereafter. Myelin volume fraction showed a reversed trend. Brain parenchymal fraction and GM fraction decreased throughout all ages. The approximation curves showed that WM fraction and MyF gradually increased until around the 40s to 50s and decreased thereafter. A significant decline in MyF was first noted in the 60s age group (Tukey test, P \textexclamdown{} 0.001). Conclusions Our study showed changes according to age in tissue characteristic values and brain volumes using quantitative synthetic MRI. The reference values for age demonstrated in this study may be useful to discriminate brain disorders from healthy brains....}
}

@article{Halchenko2021-wz,
  title = {{{DataLad}}: Distributed System for Joint Management of Code, Data, and Their Relationship},
  author = {Halchenko, Yaroslav and Meyer, Kyle and Poldrack, Benjamin and Solanky, Debanjum and Wagner, Adina and Gors, Jason and MacFarlane, Dave and Pustina, Dorian and Sochat, Vanessa and Ghosh, Satrajit and M{\"o}nch, Christian and Markiewicz, Christopher and Waite, Laura and Shlyakhter, Ilya and {de la Vega}, Alejandro and Hayashi, Soichi and H{\"a}usler, Christian and Poline, Jean-Baptiste and Kadelka, Tobias and Skyt{\'e}n, Kusti and Jarecka, Dorota and Kennedy, David and Strauss, Ted and Cieslak, Matt and Vavra, Peter and Ioanas, Horea-Ioan and Schneider, Robin and Pfl{\"u}ger, Mika and Haxby, James and Eickhoff, Simon and Hanke, Michael},
  year = {2021},
  month = jul,
  journal = {J. Open Source Softw.},
  volume = {6},
  number = {63},
  pages = {3262},
  publisher = {{The Open Journal}},
  doi = {10.21105/joss.03262}
}

@article{Herz2021-pu,
  title = {Pulseq-{{CEST}}: {{Towards}} Multi-Site Multi-Vendor Compatibility and Reproducibility of {{CEST}} Experiments Using an Open-Source Sequence Standard},
  author = {Herz, Kai and Mueller, Sebastian and Perlman, Or and Zaitsev, Maxim and Knutsson, Linda and Sun, Phillip Zhe and Zhou, Jinyuan and {van Zijl}, Peter and Heinecke, Kerstin and Schuenke, Patrick and others},
  year = {2021},
  journal = {Magnetic resonance in medicine},
  volume = {86},
  number = {4},
  pages = {1845--1858},
  publisher = {{Wiley Online Library}},
  doi = {10.1002/mrm.28825}
}

@article{Karakuzu2020-ul,
  title = {{{qMRLab}}: {{Quantitative MRI}} Analysis, under One Umbrella},
  author = {Karakuzu, Agah and Boudreau, Mathieu and Duval, Tanguy and Boshkovski, Tommy and Leppert, Ilana and Cabana, Jean-Fran{\c c}ois and Gagnon, Ian and Beliveau, Pascale and Pike, G and {Cohen-Adad}, Julien and Stikov, Nikola},
  year = {2020},
  month = sep,
  journal = {J. Open Source Softw.},
  volume = {5},
  number = {53},
  pages = {2343},
  publisher = {{The Open Journal}},
  doi = {10.21105/joss.02343}
}

@article{Karakuzu2022-af,
  title = {Vendor-Neutral Sequences and Fully Transparent Workflows Improve Inter-Vendor Reproducibility of Quantitative {{MRI}}},
  author = {Karakuzu, Agah and Biswas, Labonny and {Cohen-Adad}, Julien and Stikov, Nikola},
  year = {2022},
  month = sep,
  journal = {Magnetic Resonance in Medicine},
  volume = {88},
  number = {3},
  pages = {1212--1228},
  doi = {10.1002/mrm.29292},
  abstract = {PURPOSE: We developed an end-to-end workflow that starts with a vendor-neutral acquisition and tested the hypothesis that vendor-neutral sequences decrease inter-vendor variability of T1, magnetization transfer ratio (MTR), and magnetization transfer saturation-index (MTsat) measurements. METHODS: We developed and deployed a vendor-neutral 3D spoiled gradient-echo (SPGR) sequence on three clinical scanners by two MRI vendors. We then acquired T1 maps on the ISMRM-NIST system phantom, as well as T1, MTR, and MTsat maps in three healthy participants. We performed hierarchical shift function analysis in vivo to characterize the differences between scanners when the vendor-neutral sequence is used instead of commercial vendor implementations. Inter-vendor deviations were compared for statistical significance to test the hypothesis. RESULTS: In the phantom, the vendor-neutral sequence reduced inter-vendor differences from 8\% to 19.4\% to 0.2\% to 5\% with an overall accuracy improvement, reducing ground truth T1 deviations from 7\% to 11\% to 0.2\% to 4\%. In vivo, we found that the variability between vendors is significantly reduced (p = 0.015) for all maps (T1, MTR, and MTsat) using the vendor-neutral sequence. CONCLUSION: We conclude that vendor-neutral workflows are feasible and compatible with clinical MRI scanners. The significant reduction of inter-vendor variability using vendor-neutral sequences has important implications for qMRI research and for the reliability of multicenter clinical trials.},
  langid = {english},
  keywords = {magnetization transfer,multicenter,open source,qMRI,relaxometry,reproducibility,vendor neutral}
}

@article{Karakuzu2022-dq,
  title = {{{qMRI-BIDS}}: {{An}} Extension to the Brain Imaging Data Structure for Quantitative Magnetic Resonance Imaging Data},
  author = {Karakuzu, Agah and Appelhoff, Stefan and Auer, Tibor and Boudreau, Mathieu and Feingold, Franklin and Khan, Ali R and Lazari, Alberto and Markiewicz, Chris and Mulder, Martijn and Phillips, Christophe and Salo, Taylor and Stikov, Nikola and Whitaker, Kirstie and {de Hollander}, Gilles},
  year = {2022},
  month = aug,
  journal = {Scientific data},
  volume = {9},
  number = {1},
  pages = {517},
  doi = {10.1038/s41597-022-01571-4},
  abstract = {The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.},
  langid = {english}
}

@article{Keenan2018-px,
  title = {Quantitative Magnetic Resonance Imaging Phantoms: {{A}} Review and the Need for a System Phantom},
  author = {Keenan, Kathryn E and Ainslie, Maureen and Barker, Alex J and Boss, Michael A and Cecil, Kim M and Charles, Cecil and Chenevert, Thomas L and Clarke, Larry and Evelhoch, Jeffrey L and Finn, Paul and Gembris, Daniel and Gunter, Jeffrey L and Hill, Derek L G and Jack, Jr, Clifford R and Jackson, Edward F and Liu, Guoying and Russek, Stephen E and Sharma, Samir D and Steckner, Michael and Stupic, Karl F and Trzasko, Joshua D and Yuan, Chun and Zheng, Jie},
  year = {2018},
  month = jan,
  journal = {Magnetic Resonance in Medicine},
  volume = {79},
  number = {1},
  pages = {48--61},
  doi = {10.1002/mrm.26982},
  abstract = {The MRI community is using quantitative mapping techniques to complement qualitative imaging. For quantitative imaging to reach its full potential, it is necessary to analyze measurements across systems and longitudinally. Clinical use of quantitative imaging can be facilitated through adoption and use of a standard system phantom, a calibration/standard reference object, to assess the performance of an MRI machine. The International Society of Magnetic Resonance in Medicine AdHoc Committee on Standards for Quantitative Magnetic Resonance was established in February 2007 to facilitate the expansion of MRI as a mainstream modality for multi-institutional measurements, including, among other things, multicenter trials. The goal of the Standards for Quantitative Magnetic Resonance committee was to provide a framework to ensure that quantitative measures derived from MR data are comparable over time, between subjects, between sites, and between vendors. This paper, written by members of the Standards for Quantitative Magnetic Resonance committee, reviews standardization attempts and then details the need, requirements, and implementation plan for a standard system phantom for quantitative MRI. In addition, application-specific phantoms and implementation of quantitative MRI are reviewed. Magn Reson Med 79:48-61, 2018.  2017 International Society for Magnetic Resonance in Medicine.},
  langid = {english},
  keywords = {phantom,quality assurance,quantitative,system consistency}
}

@article{Keenan2021-ly,
  title = {Multi-Site, Multi-Platform Comparison of {{MRI T1}} Measurement Using the System Phantom},
  author = {Keenan, Kathryn E and Gimbutas, Zydrunas and Dienstfrey, Andrew and Stupic, Karl F and Boss, Michael A and Russek, Stephen E and Chenevert, Thomas L and Prasad, P V and Guo, Junyu and Reddick, Wilburn E and Cecil, Kim M and {Shukla-Dave}, Amita and Aramburu Nunez, David and Shridhar Konar, Amaresh and Liu, Michael Z and Jambawalikar, Sachin R and Schwartz, Lawrence H and Zheng, Jie and Hu, Peng and Jackson, Edward F},
  year = {2021},
  month = jun,
  journal = {PLoS One},
  volume = {16},
  number = {6},
  pages = {e0252966},
  doi = {10.1371/journal.pone.0252966},
  abstract = {Recent innovations in quantitative magnetic resonance imaging (MRI) measurement methods have led to improvements in accuracy, repeatability, and acquisition speed, and have prompted renewed interest to reevaluate the medical value of quantitative T1. The purpose of this study was to determine the bias and reproducibility of T1 measurements in a variety of MRI systems with an eye toward assessing the feasibility of applying diagnostic threshold T1 measurement across multiple clinical sites. We used the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom to assess variations of T1 measurements, using a slow, reference standard inversion recovery sequence and a rapid, commonly-available variable flip angle sequence, across MRI systems at 1.5 tesla (T) (two vendors, with number of MRI systems n = 9) and 3 T (three vendors, n = 18). We compared the T1 measurements from inversion recovery and variable flip angle scans to ISMRM/NIST phantom reference values using Analysis of Variance (ANOVA) to test for statistical differences between T1 measurements grouped according to MRI scanner manufacturers and/or static field strengths. The inversion recovery method had minor over- and under-estimations compared to the NMR-measured T1 values at both 1.5 T and 3 T. Variable flip angle measurements had substantially greater deviations from the NMR-measured T1 values than the inversion recovery measurements. At 3 T, the measured variable flip angle T1 for one vendor is significantly different than the other two vendors for most of the samples throughout the clinically relevant range of T1. There was no consistent pattern of discrepancy between vendors. We suggest establishing rigorous quality control procedures for validating quantitative MRI methods to promote confidence and stability in associated measurement techniques and to enable translation of diagnostic threshold from the research center to the entire clinical community.},
  langid = {english}
}

@article{Layton2017-qz,
  title = {Pulseq: {{A}} Rapid and Hardware-Independent Pulse Sequence Prototyping Framework},
  author = {Layton, Kelvin J and Kroboth, Stefan and Jia, Feng and Littin, Sebastian and Yu, Huijun and Leupold, Jochen and Nielsen, Jon-Fredrik and St{\"o}cker, Tony and Zaitsev, Maxim},
  year = {2017},
  month = apr,
  journal = {Magnetic Resonance in Medicine},
  volume = {77},
  number = {4},
  pages = {1544--1552},
  publisher = {{Wiley}},
  doi = {10.1002/mrm.26235},
  langid = {english}
}

@article{Lee2019-tn,
  title = {Establishing Intra- and Inter-Vendor Reproducibility of {{T1}} Relaxation Time Measurements with {{3T MRI}}},
  author = {Lee, Yoojin and Callaghan, Martina F and {Acosta-Cabronero}, Julio and Lutti, Antoine and Nagy, Zoltan},
  year = {2019},
  month = jan,
  journal = {Magnetic Resonance in Medicine},
  volume = {81},
  number = {1},
  pages = {454--465},
  doi = {10.1002/mrm.27421},
  abstract = {PURPOSE: Parametric imaging methods (e.g., T1 relaxation time mapping) have been shown to be more reproducible across time and vendors than weighted (e.g., T1 -weighted) images. The purpose of this work was to more extensively evaluate the validity of this assertion. METHODS: Seven volunteers underwent twice-repeated acquisitions of variable flip-angle T1 mapping, including B1 + calibration, on a 3T Philips Achieva and 3T Siemens Trio scanner. Intra-scanner and inter-vendor T1 variability were calculated. To determine T1 reproducibility levels in longitudinal settings, or after changing hardware or software, four additional data sets were acquired from two of the participants; one participant was scanned on a different 3T Siemens Trio scanner and another on the same 3T Philips Achieva scanner but after a software upgrade. RESULTS: Intra-scanner variability of voxel-wise T1 values was consistent between the two vendors, averaging 0.7/0.7/1.3/1.4\% in white matter/cortical gray matter/subcortical gray matter/cerebellum, respectively. We observed, however, a systematic bias between the two vendors of https://doi.org/10.0/7.8/8.6/10.0\%, respectively. The T1 bias across two scanners of the same model was greater than intra-scanner variability, although still only at 1.4/1.0/1.9/2.3\%, respectively. A greater bias was identified for data sets acquired before/after software upgrade in white matter/cortical gray matter (3.6/2.7\%) whereas variability in subcortical gray matter/cerebellum was comparable (1.7/1.9\%). CONCLUSION: We established intra- and inter-vendor reproducibility levels for a widely used T1 mapping protocol. We anticipate that these results will guide the design of multi-center studies, particularly those encompassing multiple vendors. Furthermore, this baseline level of reproducibility should be established or surpassed during the piloting phase of such studies.},
  langid = {english},
  keywords = {3T,bias,multi-vendor,parametric imaging,reproducibility,T1 relaxation}
}

@article{Leutritz2020-wc,
  title = {Multiparameter Mapping of Relaxation ({{R1}}, {{R2}}*), Proton Density and Magnetization Transfer Saturation at 3 {{T}}: {{A}} Multicenter Dual-Vendor Reproducibility and Repeatability Study},
  author = {Leutritz, Tobias and Seif, Maryam and Helms, Gunther and Samson, Rebecca S and Curt, Armin and Freund, Patrick and Weiskopf, Nikolaus},
  year = {2020},
  month = oct,
  journal = {Human Brain Mapping},
  volume = {41},
  number = {15},
  pages = {4232--4247},
  doi = {10.1002/hbm.25122},
  abstract = {Multicenter clinical and quantitative magnetic resonance imaging (qMRI) studies require a high degree of reproducibility across different sites and scanner manufacturers, as well as time points. We therefore implemented a multiparameter mapping (MPM) protocol based on vendor's product sequences and demonstrate its repeatability and reproducibility for whole-brain coverage. Within  20 min, four MPM metrics (magnetization transfer saturation [MT], proton density [PD], longitudinal [R1], and effective transverse [R2*] relaxation rates) were measured using an optimized 1 mm isotropic resolution protocol on six 3 T MRI scanners from two different vendors. The same five healthy participants underwent two scanning sessions, on the same scanner, at each site. MPM metrics were calculated using the hMRI-toolbox. To account for different MT pulses used by each vendor, we linearly scaled the MT values to harmonize them across vendors. To determine longitudinal repeatability and inter-site comparability, the intra-site (i.e., scan-rescan experiment) coefficient of variation (CoV), inter-site CoV, and bias across sites were estimated. For MT, R1, and PD, the intra- and inter-site CoV was between 4 and 10\% across sites and scan time points for intracranial gray and white matter. A higher intra-site CoV (16\%) was observed in R2* maps. The inter-site bias was below 5\% for all parameters. In conclusion, the MPM protocol yielded reliable quantitative maps at high resolution with a short acquisition time. The high reproducibility of MPM metrics across sites and scan time points combined with its tissue microstructure sensitivity facilitates longitudinal multicenter imaging studies targeting microstructural changes, for example, as a quantitative MRI biomarker for interventional clinical trials.},
  langid = {english},
  keywords = {clinical trial,in vivo histology using MRI,multicenter study,multiparameter mapping,quantitative MRI,reproducibility}
}

@article{Levy2015-tt,
  title = {White Matter Atlas of the Human Spinal Cord with Estimation of Partial Volume Effect},
  author = {L{\'e}vy, S and Benhamou, M and Naaman, C and Rainville, P and Callot, V and {Cohen-Adad}, J},
  year = {2015},
  month = oct,
  journal = {Neuroimage},
  volume = {119},
  pages = {262--271},
  doi = {10.1016/j.neuroimage.2015.06.040},
  abstract = {Template-based analysis has proven to be an efficient, objective and reproducible way of extracting relevant information from multi-parametric MRI data. Using common atlases, it is possible to quantify MRI metrics within specific regions without the need for manual segmentation. This method is therefore free from user-bias and amenable to group studies. While template-based analysis is common procedure for the brain, there is currently no atlas of the white matter (WM) spinal pathways. The goals of this study were: (i) to create an atlas of the white matter tracts compatible with the MNI-Poly-AMU template and (ii) to propose methods to quantify metrics within the atlas that account for partial volume effect. The WM atlas was generated by: (i) digitalizing an existing WM atlas from a well-known source (Gray's Anatomy), (ii) registering this atlas to the MNI-Poly-AMU template at the corresponding slice (C4 vertebral level), (iii) propagating the atlas throughout all slices of the template (C1 to T6) using regularized diffeomorphic transformations and (iv) computing partial volume values for each voxel and each tract. Several approaches were implemented and validated to quantify metrics within the atlas, including weighted-average and Gaussian mixture models. Proof-of-concept application was done in five subjects for quantifying magnetization transfer ratio (MTR) in each tract of the atlas. The resulting WM atlas showed consistent topological organization and smooth transitions along the rostro-caudal axis. The median MTR across tracts was 26.2. Significant differences were detected across tracts, vertebral levels and subjects, but not across laterality (right-left). Among the different tested approaches to extract metrics, the maximum a posteriori showed highest performance with respect to noise, inter-tract variability, tract size and partial volume effect. This new WM atlas of the human spinal cord overcomes the biases associated with manual delineation and partial volume effect. Combined with multi-parametric data, the atlas can be applied to study demyelination and degeneration in diseases such as multiple sclerosis and will facilitate the conduction of longitudinal and multi-center studies.},
  langid = {english},
  keywords = {Atlas,MRI,Spinal cord,Template,White matter}
}

@article{Levy2018-gt,
  title = {Test-Retest Reliability of Myelin Imaging in the Human Spinal Cord: {{Measurement}} Errors versus Region- and Aging-Induced Variations},
  author = {L{\'e}vy, Simon and Guertin, Marie-Claude and Khatibi, Ali and Mezer, Aviv and Martinu, Kristina and Chen, Jen-I and Stikov, Nikola and Rainville, Pierre and {Cohen-Adad}, Julien},
  year = {2018},
  month = jan,
  journal = {PLoS One},
  volume = {13},
  number = {1},
  pages = {e0189944},
  doi = {10.1371/journal.pone.0189944},
  abstract = {PURPOSE: To implement a statistical framework for assessing the precision of several quantitative MRI metrics sensitive to myelin in the human spinal cord: T1, Magnetization Transfer Ratio (MTR), saturation imposed by an off-resonance pulse (MTsat) and Macromolecular Tissue Volume (MTV). METHODS: Thirty-three healthy subjects within two age groups (young, elderly) were scanned at 3T. Among them, 16 underwent the protocol twice to assess repeatability. Statistical reliability indexes such as the Minimal Detectable Change (MDC) were compared across metrics quantified within different cervical levels and white matter (WM) sub-regions. The differences between pathways and age groups were quantified and interpreted in context of the test-retest repeatability of the measurements. RESULTS: The MDC was respectively 105.7ms, 2.77\%, 0.37\% and 4.08\% for T1, MTR, MTsat and MTV when quantified over all WM, while the standard-deviation across subjects was 70.5ms, 1.34\%, 0.20\% and 2.44\%. Even though particular WM regions did exhibit significant differences, these differences were on the same order as test-retest errors. No significant difference was found between age groups for all metrics. CONCLUSION: While T1-based metrics (T1 and MTV) exhibited better reliability than MT-based measurements (MTR and MTsat), the observed differences between subjects or WM regions were comparable to (and often smaller than) the MDC. This makes it difficult to determine if observed changes are due to variations in myelin content, or simply due to measurement error. Measurement error remains a challenge in spinal cord myelin imaging, but this study provides statistical guidelines to standardize the field and make it possible to conduct large-scale multi-center studies.},
  langid = {english}
}

@article{Liden2021-el,
  title = {Quantitative {{T2}}* Imaging of Iron Overload in a Non-Dedicated Center - {{Normal}} Variation, Repeatability and Reader Variation},
  author = {Lid{\'e}n, Mats and Adrian, David and Widell, Jonas and Uggla, Bertil and Thunberg, Per},
  year = {2021},
  month = may,
  journal = {European journal of radiology open},
  volume = {8},
  pages = {100357},
  abstract = {BACKGROUND: Patients with transfusion dependent anemia are at risk of complications from iron overload. Quantitative T2* magnetic resonance imaging (MRI) is the best non-invasive method to assess iron deposition in the liver and heart and to guide chelation therapy. PURPOSE: To investigate the image quality and inter-observer variations in T2* measurements of the myocardium and the liver, and to obtain the lower limit of cardiac and hepatic quantitative T2* values in patients without suspicion of iron overload. MATERIAL AND METHODS: Thirty-eight patients referred for cardiac MRI were prospectively included in the study. Three patients were referred with, and 35 without suspicion of iron overload. Quantitative T2* parametric maps were obtained on a 1.5 T MRI system in the cardiac short axis and liver axial view. Two readers independently assessed the image quality and the representative and the lowest T2* value in the myocardium and the liver. RESULTS: The normal range of representative T2* values in the myocardium and liver was 24-45 ms and 14-37 ms, respectively. None of the 35 participants (0 \%, 95 \% confidence interval 0-11 \%) in the normal reference group demonstrated representative T2* values below previously reported lower limits in the myocardium (20 ms) or the liver (8 ms). Focal myocardial areas with T2* values near the lower normal range, 19-20 ms, were seen in two patients. The readers generally reported good image quality. CONCLUSION: T2* imaging for assessing iron overload can be performed in a non-dedicated center with sufficient image quality.},
  langid = {english},
  doi = {10.1016/j.ejro.2021.100357}
}

@article{Ma2013-kv,
  title = {Magnetic Resonance Fingerprinting},
  author = {Ma, Dan and Gulani, Vikas and Seiberlich, Nicole and Liu, Kecheng and Sunshine, Jeffrey L and Duerk, Jeffrey L and Griswold, Mark A},
  year = {2013},
  month = mar,
  journal = {Nature},
  volume = {495},
  number = {7440},
  pages = {187--192},
  doi = {10.1038/nature11971},
  abstract = {Magnetic resonance is an exceptionally powerful and versatile measurement technique. The basic structure of a magnetic resonance experiment has remained largely unchanged for almost 50 years, being mainly restricted to the qualitative probing of only a limited set of the properties that can in principle be accessed by this technique. Here we introduce an approach to data acquisition, post-processing and visualization\textendash which we term 'magnetic resonance fingerprinting' (MRF)\textendash that permits the simultaneous non-invasive quantification of multiple important properties of a material or tissue. MRF thus provides an alternative way to quantitatively detect and analyse complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to identify the presence of a specific target material or tissue, which will increase the sensitivity, specificity and speed of a magnetic resonance study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern-recognition algorithm, MRF inherently suppresses measurement errors and can thus improve measurement accuracy.},
  langid = {english}
}

@article{Ma2016-fo,
  title = {The Effect of Dissolved Oxygen on the Relaxation Rates of Blood Plasma: {{Implications}} for Hyperoxia Calibrated {{BOLD}}},
  author = {Ma, Yuhan and Berman, Avery J L and Pike, G Bruce},
  year = {2016},
  month = dec,
  journal = {Magnetic Resonance in Medicine},
  volume = {76},
  number = {6},
  pages = {1905--1911},
  publisher = {{Wiley}},
  doi = {10.1002/mrm.26069},
  abstract = {Purpose To determine the contribution of paramagnetic dissolved oxygen in blood plasma to blood-oxygenation-level-dependent (BOLD) signal changes in hyperoxic calibrated BOLD studies. Methods Bovine blood plasma samples were prepared with partial pressures of oxygen (pO2) ranging from 110 to 600 mmHg. R1, R2, and R2* of the plasma with dissolved oxygen were measured using quantitative MRI sequences at 3 Tesla. Simulations were performed to predict the relative effects of dissolved oxygen and deoxyhemoglobin changes in hyperoxia calibrated BOLD. Results The relaxivities of dissolved oxygen in plasma were found to be r1, O2 =1.97 {$\pm$} 0.09 ?10-4 s-1mmHg-1, r2, O2 =2.3 {$\pm$} 0.7 ?10-4 s-1mmHg-1, and r2, O2* = 2.3 {$\pm$} 0.7 ?10-4 s-1mmHg-1. Simulations predict that neither the transverse nor longitudinal relaxation rates of dissolved oxygen contribute significantly to the BOLD signal during hyperoxia. Conclusion During hyperoxia, the increases in R2 and R2* of blood from dissolved oxygen in plasma are considerably less than the decreases in R2 and R2* from venous deoxyhemoglobin. R1 effects due to dissolved oxygen are also predicted to be negligible. As a result, dissolved oxygen in arteries should not contribute significantly to the hyperoxic calibrated BOLD signal. Magn Reson Med 76:1905?1911, 2016. ? 2015 International Society for Magnetic Resonance in Medicine},
  langid = {english}
}

@article{MacDonald2021-uc,
  title = {{{MRI}} of Healthy Brain Aging: {{A}} Review},
  author = {MacDonald, M Ethan and Pike, G Bruce},
  year = {2021},
  month = sep,
  journal = {NMR in Biomedicine},
  volume = {34},
  number = {9},
  pages = {e4564},
  doi = {10.1002/nbm.4564},
  abstract = {We present a review of the characterization of healthy brain aging using MRI with an emphasis on morphology, lesions, and quantitative MR parameters. A scope review found 6612 articles encompassing the keywords ``Brain Aging'' and ``Magnetic Resonance''; papers involving functional MRI or not involving imaging of healthy human brain aging were discarded, leaving 2246 articles. We first consider some of the biogerontological mechanisms of aging, and the consequences of aging in terms of cognition and onset of disease. Morphological changes with aging are reviewed for the whole brain, cerebral cortex, white matter, subcortical gray matter, and other individual structures. In general, volume and cortical thickness decline with age, beginning in mid-life. Prevalent silent lesions such as white matter hyperintensities, microbleeds, and lacunar infarcts are also observed with increasing frequency. The literature regarding quantitative MR parameter changes includes T1 , T2 , T2 *, magnetic susceptibility, spectroscopy, magnetization transfer, diffusion, and blood flow. We summarize the findings on how each of these parameters varies with aging. Finally, we examine how the aforementioned techniques have been used for age prediction. While relatively large in scope, we present a comprehensive review that should provide the reader with sound understanding of what MRI has been able to tell us about how the healthy brain ages.},
  langid = {english}
}

@article{Mancini2020-sv,
  title = {An Interactive Meta-Analysis of {{MRI}} Biomarkers of Myelin},
  author = {Mancini, Matteo and Karakuzu, Agah and {Cohen-Adad}, Julien and Cercignani, Mara and Nichols, Thomas E and Stikov, Nikola},
  year = {2020},
  month = oct,
  journal = {Elife},
  volume = {9},
  abstract = {Several MRI measures have been proposed as in vivo biomarkers of myelin, each with applications ranging from plasticity to pathology. Despite the availability of these myelin-sensitive modalities, specificity and sensitivity have been a matter of discussion. Debate about which MRI measure is the most suitable for quantifying myelin is still ongoing. In this study, we performed a systematic review of published quantitative validation studies to clarify how different these measures are when compared to the underlying histology. We analyzed the results from 43 studies applying meta-analysis tools, controlling for study sample size and using interactive visualization (https://neurolibre.github.io/myelin-meta-analysis). We report the overall estimates and the prediction intervals for the coefficient of determination and find that MT and relaxometry-based measures exhibit the highest correlations with myelin content. We also show which measures are, and which measures are not statistically different regarding their relationship with histology.},
  langid = {english},
  doi = {10.7554/eLife.61523}
}

@misc{Marques2010-po,
  title = {{{MP2RAGE}}, a Self Bias-Field Corrected Sequence for Improved Segmentation and {{T1-mapping}} at High Field},
  author = {Marques, Jos{\'e} P and Kober, Tobias and Krueger, Gunnar and {van der Zwaag}, Wietske and {Van de Moortele}, Pierre-Fran{\c c}ois and Gruetter, Rolf},
  year = {2010},
  journal = {NeuroImage},
  volume = {49},
  number = {2},
  pages = {1271--1281},
  doi = {10.1016/j.neuroimage.2009.10.002}
}

@article{Marques2013-yg,
  title = {New Developments and Applications of the {{MP2RAGE}} Sequence\textendash Focusing the Contrast and High Spatial Resolution {{R1}} Mapping},
  author = {Marques, Jos{\'e} P and Gruetter, Rolf},
  year = {2013},
  month = jul,
  journal = {PLoS One},
  volume = {8},
  number = {7},
  pages = {e69294},
  doi = {10.1371/journal.pone.0069294},
  abstract = {MR structural T1-weighted imaging using high field systems (\textquestiondown 3T) is severely hampered by the existing large transmit field inhomogeneities. New sequences have been developed to better cope with such nuisances. In this work we show the potential of a recently proposed sequence, the MP2RAGE, to obtain improved grey white matter contrast with respect to conventional T1-w protocols, allowing for a better visualization of thalamic nuclei and different white matter bundles in the brain stem. Furthermore, the possibility to obtain high spatial resolution (0.65 mm isotropic) R1 maps fully independent of the transmit field inhomogeneities in clinical acceptable time is demonstrated. In this high resolution R1 maps it was possible to clearly observe varying properties of cortical grey matter throughout the cortex and observe different hippocampus fields with variations of intensity that correlate with known myelin concentration variations.},
  langid = {english}
}

@article{Mazerolle2018-xy,
  title = {Impact of Abnormal Cerebrovascular Reactivity on {{BOLD fMRI}}: A Preliminary Investigation of Moyamoya Disease},
  author = {Mazerolle, Erin L and Ma, Yuhan and Sinclair, David and Pike, G Bruce},
  year = {2018},
  month = jan,
  journal = {Clinical Physiology and Functional Imaging},
  volume = {38},
  number = {1},
  pages = {87--92},
  doi = {10.1111/cpf.12387},
  abstract = {Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies of patients with cerebrovascular disease have largely ignored the confounds associated with abnormal cerebral blood flow, vascular reactivity and neurovascular coupling. We studied BOLD fMRI activation and cerebrovascular reactivity in moyamoya disease. To characterize the impact of remote vascular demands on BOLD fMRI measurements, we varied the vascular territories engaged by manipulating the experimental task performed by the participants. Vascular territories affected by disease were identified using BOLD cerebrovascular reactivity. Preliminary evidence from two patients pre- and postrevascularization surgery and four controls indicates that neurovascular coupling in affected brain regions can be modulated by the task-related vascular demands in unaffected regions. In one patient studied, we observed that brain regions with improved cerebrovascular reactivity after surgery demonstrated normalized neurovascular coupling, that is the degree to which neurovascular coupling was modulated by task-related vascular demands was decreased. We propose that variations in task-dependent neurovascular coupling in patients with moyamoya disease are likely related to vascular steal. While preliminary, our findings are a proof of concept of the limitations of BOLD fMRI in cerebrovascular disease and suggest a role for assessment of cerebrovascular reactivity to improve interpretation of task-related BOLD fMRI activation.},
  langid = {english},
  keywords = {cerebrovascular disease,functional brain mapping,hypercapnia,neurovascular coupling,stenosis}
}

@article{Meyers2016-rc,
  title = {Does Hydration Status Affect {{MRI}} Measures of Brain Volume or Water Content?},
  author = {Meyers, Sandra M and Tam, Roger and Lee, Jimmy S and Kolind, Shannon H and Vavasour, Irene M and Mackie, Emilie and Zhao, Yinshan and Laule, Cornelia and M{\"a}dler, Burkhard and Li, David K B and MacKay, Alex L and Traboulsee, Anthony L},
  year = {2016},
  month = aug,
  journal = {Journal of Magnetic Resonance Imaging},
  volume = {44},
  number = {2},
  pages = {296--304},
  doi = {10.1002/jmri.25168},
  abstract = {PURPOSE: To determine whether differences in hydration state, which could arise from routine clinical procedures such as overnight fasting, affect brain total water content (TWC) and brain volume measured with magnetic resonance imaging (MRI). MATERIALS AND METHODS: Twenty healthy volunteers were scanned with a 3T MR scanner four times: day 1, baseline scan; day 2, hydrated scan after consuming 3L of water over 12 hours; day 3, dehydrated scan after overnight fasting of 9 hours, followed by another scan 1 hour later for reproducibility. The following MRI data were collected: T2 relaxation (for TWC measurement), inversion recovery (for T1 measurement), and 3D T1 -weighted (for brain volumes). Body weight and urine specific gravity were also measured. TWC was calculated by fitting the T2 relaxation data with a nonnegative least-squares algorithm, with corrections for T1 relaxation and image signal inhomogeneity and normalization to ventricular cerebrospinal fluid. Brain volume changes were measured using SIENA. TWC means were calculated within 14 tissue regions. RESULTS: Despite indications of dehydration as demonstrated by increases in urine specific gravity (P = 0.03) and decreases in body weight (P = 0.001) between hydrated and dehydrated scans, there was no measurable change in TWC (within any brain region) or brain volume between hydration states. CONCLUSION: We demonstrate that within a range of physiologic conditions commonly encountered in routine clinical scans (no pretreatment with hydration, well hydrated before MRI, and overnight fasting), brain TWC and brain volumes are not substantially affected in a healthy control cohort. J. Magn. Reson. Imaging 2016;44:296-304.},
  langid = {english},
  keywords = {brain volume,hydration,magnetic resonance,proton density,T2 relaxation,water content}
}

@article{Nigri2022-kg,
  title = {Quantitative {{MRI}} Harmonization to Maximize Clinical Impact: {{The RIN-Neuroimaging}} Network},
  author = {Nigri, Anna and Ferraro, Stefania and {Gandini Wheeler-Kingshott}, Claudia A M and Tosetti, Michela and Redolfi, Alberto and Forloni, Gianluigi and D'Angelo, Egidio and Aquino, Domenico and Biagi, Laura and Bosco, Paolo and Carne, Irene and De Francesco, Silvia and Demichelis, Greta and Gianeri, Ruben and Lagana, Maria Marcella and Micotti, Edoardo and Napolitano, Antonio and Palesi, Fulvia and Pirastru, Alice and Savini, Giovanni and Alberici, Elisa and Amato, Carmelo and Arrigoni, Filippo and Baglio, Francesca and Bozzali, Marco and Castellano, Antonella and Cavaliere, Carlo and Contarino, Valeria Elisa and Ferrazzi, Giulio and Gaudino, Simona and Marino, Silvia and Manzo, Vittorio and Pavone, Luigi and Politi, Letterio S and Roccatagliata, Luca and Rognone, Elisa and Rossi, Andrea and Tonon, Caterina and Lodi, Raffaele and Tagliavini, Fabrizio and Bruzzone, Maria Grazia and {RIN\textendash Neuroimaging}},
  year = {2022},
  month = apr,
  journal = {Frontiers in Neurology},
  volume = {13},
  pages = {855125},
  abstract = {Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., {$\geq$}3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures.},
  langid = {english},
  doi = {10.3389/fneur.2022.855125}
}

@article{Oh2021-os,
  title = {Five-Year Longitudinal Changes in Quantitative Spinal Cord {{MRI}} in Multiple Sclerosis},
  author = {Oh, Jiwon and Chen, Min and Cybulsky, Kateryna and Suthiphosuwan, Suradech and Seyman, Estelle and Dewey, Blake and {Diener-West}, Marie and {van Zijl}, Peter and Prince, Jerry and Reich, Daniel S and Calabresi, Peter A},
  year = {2021},
  month = apr,
  journal = {Multiple Sclerosis},
  volume = {27},
  number = {4},
  pages = {549--558},
  doi = {10.1177/1352458520923970},
  abstract = {BACKGROUND: The spinal cord (SC) is highly relevant to disability in multiple sclerosis (MS), but few studies have evaluated longitudinal changes in quantitative spinal cord magnetic resonance imaging (SC-MRI). OBJECTIVES: The aim of this study was to characterize the relationships between 5-year changes in SC-MRI with disability in MS. METHODS: In total, 75 MS patients underwent 3 T SC-MRI and clinical assessment (expanded disability status scale (EDSS) and MS functional composite (MSFC)) at baseline, 2 and 5 years. SC-cross-sectional area (CSA) and diffusion-tensor indices (fractional anisotropy (FA), mean, perpendicular, parallel diffusivity (MD, {$\lambda$}, {$\lambda$}||) and magnetization transfer ratio (MTR)) were extracted at C3-C4. Mixed-effects regression incorporating subject-specific slopes assessed longitudinal change in SC-MRI measures. RESULTS: SC-CSA and MTR decreased (p = 0.009, p = 0.03) over 5.1 years. There were moderate correlations between 2- and 5-year subject-specific slopes of SC-MRI indices and follow-up EDSS scores (Pearson's r with FA = -0.23 (p \textexclamdown{} 0.001); MD = 0.31 (p \textexclamdown{} 0.001); {$\lambda$} = 0.34 (p \textexclamdown{} 0.001); {$\lambda$}|| = -0.12 (p = 0.05), MTR = -0.37 (p \textexclamdown{} 0.001); SC-CSA = -0.47 (p \textexclamdown{} 0.001) at 5 years); MSFC showed similar trends. The 2- and 5-year subject-specific slopes were robustly correlated (r = 0.93-0.97 for FA, {$\lambda$}, SC-CSA and MTR, all ps \textexclamdown{} 0.001). CONCLUSION: In MS, certain quantitative SC-MRI indices change over 5 years, reflecting ongoing tissue changes. Subject-specific trajectories of SC-MRI index change at 2 and 5 years are strongly correlated and highly relevant to follow-up disability. These findings suggest that individual dynamics of change should be accounted for when interpreting longitudinal SC-MRI measures and that measuring short-term change is predictive of long-term clinical disability.},
  langid = {english},
  keywords = {diffusion-tensor imaging,magnetization-transfer imaging,Multiple sclerosis,quantitative MRI,spinal cord}
}

@article{Oishi2009-nj,
  title = {Atlas-Based Whole Brain White Matter Analysis Using Large Deformation Diffeomorphic Metric Mapping: Application to Normal Elderly and {{Alzheimer}}'s Disease Participants},
  author = {Oishi, Kenichi and Faria, Andreia and Jiang, Hangyi and Li, Xin and Akhter, Kazi and Zhang, Jiangyang and Hsu, John T and Miller, Michael I and {van Zijl}, Peter C M and Albert, Marilyn and Lyketsos, Constantine G and Woods, Roger and Toga, Arthur W and Pike, G Bruce and {Rosa-Neto}, Pedro and Evans, Alan and Mazziotta, John and Mori, Susumu},
  year = {2009},
  month = jun,
  journal = {Neuroimage},
  volume = {46},
  number = {2},
  pages = {486--499},
  doi = {10.1016/j.neuroimage.2009.01.002},
  abstract = {The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer's disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0.70 for both normal controls and patients while the inter-rater reproducibility was 0.81 (controls) and 0.82 (patients), suggesting ``almost perfect'' agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.},
  langid = {english}
}

@misc{Papp2016-mz,
  title = {Correction of Inter-scan Motion Artifacts in Quantitative {{R1}} Mapping by Accounting for Receive Coil Sensitivity Effects},
  author = {Papp, Daniel and Callaghan, Martina F and Meyer, Heiko and Buckley, Craig and Weiskopf, Nikolaus},
  year = {2016},
  volume = {76},
  number = {5},
  pages = {1478--1485},
  doi = {10.1002/mrm.26058},
  journal = {Magnetic Resonance in Medicine}
}

@article{Perone2018-la,
  title = {Spinal Cord Gray Matter Segmentation Using Deep Dilated Convolutions},
  author = {Perone, Christian S and Calabrese, Evan and {Cohen-Adad}, Julien},
  year = {2018},
  month = apr,
  journal = {Scientific Reports},
  volume = {8},
  number = {1},
  pages = {5966},
  doi = {10.1038/s41598-018-24304-3},
  abstract = {Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and were recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully-automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge. We report state-of-the-art results in 8 out of 10 evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.},
  langid = {english}
}

@misc{Plotly_Technologies_Inc2015-gp,
  title = {Collaborative Data Science},
  author = {{Plotly Technologies Inc.}},
  year = {2015}
}

@article{Pykett1978-ef,
  title = {A Line Scan Image Study of a Tumorous Rat Leg by {{NMR}}},
  author = {Pykett, I L and Mansfield, P},
  year = {1978},
  month = sep,
  journal = {Physics in Medicine and Biology},
  volume = {23},
  number = {5},
  pages = {961--967},
  doi = {10.1088/0031-9155/23/5/012},
  langid = {english}
}

@article{Ropele2011-zl,
  title = {{{MRI}} Assessment of Iron Deposition in Multiple Sclerosis},
  author = {Ropele, Stefan and {de Graaf}, Wolter and Khalil, Michael and Wattjes, Mike P and Langkammer, Christian and Rocca, Maria A and Rovira, Alex and Palace, Jacqueline and Barkhof, Frederik and Filippi, Massimo and Fazekas, Franz},
  year = {2011},
  month = jul,
  journal = {Journal of Magnetic Resonance Imaging},
  volume = {34},
  number = {1},
  pages = {13--21},
  doi = {10.1002/jmri.22590},
  abstract = {Iron deposition in the human brain tissue occurs in the process of normal aging and in many neurodegenerative diseases. Elevated iron levels in certain brain regions are also an increasingly recognized finding in multiple sclerosis (MS). The exact mechanism(s) for this phenomenon and its implication in terms of pathophysiology and clinical significance are still largely unknown and debated. Reliable methods to exactly quantify brain iron are a first step to clarify these issues. Therefore, the aim of this review is to present currently available magnetic resonance imaging (MRI) techniques for the assessment of brain iron. These include relaxation time mapping, phase imaging, susceptibility-weighted imaging, susceptibility mapping, magnetic field correlation imaging, and direct saturation imaging. After discussing their advantages and disadvantages, existing MRI clinical correlations with brain iron concentration in MS are summarized and future research directions are shown.},
  langid = {english}
}

@article{Salluzzi2022-jc,
  title = {Short-Term Repeatability and Long-Term Reproducibility of Quantitative {{MR}} Imaging Biomarkers in a Single Centre Longitudinal Study},
  author = {Salluzzi, Marina and McCreary, Cheryl R and Gobbi, David G and Lauzon, Michel Louis and Frayne, Richard},
  year = {2022},
  month = oct,
  journal = {NeuroImage},
  doi = {10.1016/j.neuroimage.2022.119488},
  volume = {260},
  pages = {119488},
  abstract = {Quantitative imaging biomarkers (QIBs) can be defined as objective measures that are sensitive and specific to changes in tissue physiology. Provided the acquired QIBs are not affected by scanner changes, they could play an important role in disease diagnosis, prognosis, management, and treatment monitoring. The precision of selected QIBs was assessed from data collected on a 3-T scanner in four healthy participants over a 5-year period. Inevitable scanner changes and acquisition protocol revisions occurred during this time. Standard and custom processing pipelines were used to calculate regional brain volume, cortical thickness, T2, T2*, quantitative susceptibility, cerebral blood flow, axial, radial and mean diffusivity, peak width of skeletonized mean diffusivity, and fractional anisotropy from the acquired images. Coefficient of variation (CoV) and intra-class correlation (ICC) indices were determined in the short-term (i.e., repeatable over three acquisitions within 4 weeks) and in the long-term (i.e., reproducible over four acquisition sessions in 5 years). Precision indices varied based on acquisition technique, processing pipeline, and anatomical region. Good repeatability (average CoV=2.40\% and ICC=0.78) and reproducibility (average CoV=8.86 \% and ICC=0.72) were found over all QIBs. The best performance indices were obtained for diffusion derived biomarkers (CoV{$\sim$}0.96\% and ICCs=0.87); conversely, the poorest indices were found for the cerebral blood flow biomarker (CoV\textquestiondown 10\% and ICC\textexclamdown 0.5). These results demonstrate that changes in protocol, along with hardware and software upgrades, did not affect the estimates of the selected biomarkers and their precision. Further characterization of the QIB is necessary to understand meaningful changes in the biomarkers in longitudinal studies of normal brain aging and translation to clinical research.},
  langid = {english}
}

@article{Schmierer2007-mb,
  title = {Quantitative Magnetization Transfer Imaging in Postmortem Multiple Sclerosis Brain},
  author = {Schmierer, Klaus and Tozer, Daniel J and Scaravilli, Francesco and Altmann, Daniel R and Barker, Gareth J and Tofts, Paul S and Miller, David H},
  year = {2007},
  month = jul,
  journal = {Journal of Magnetic Resonance Imaging},
  volume = {26},
  number = {1},
  pages = {41--51},
  doi = {10.1002/jmri.20984},
  abstract = {PURPOSE: To investigate the relationship of myelin content, axonal density, and gliosis with the fraction of macromolecular protons (fB) and T2 relaxation of the macromolecular pool (T2B) acquired using quantitative magnetization transfer (qMT) MRI in postmortem brains of subjects with multiple sclerosis (MS). MATERIALS AND METHODS: fB and T2B were acquired in unfixed postmortem brain slices of 20 subjects with MS. The myelin content, axonal count, and severity of gliosis were all quantified histologically. t-Tests and multiple regression were used for analysis. RESULTS: MR indices obtained in unfixed postmortem MS brains were consistent with in vivo values reported in the literature. A significant correlation was detected between Tr(myelin) (inversely proportional to myelin content) and 1) fB (r = -0.80, P \textexclamdown{} 0.001) and 2) axonal count (r = -0.79, P \textexclamdown{} 0.001). fB differed between 1) normal-appearing white matter (NAWM) and remyelinated WM lesions (rWMLs) (mean: fB 6.9 [SD 2] vs. 4.0 [1.8], P = 0.01), and 2) rWMLs and demyelinated WMLs (mean: 4.2 [2.2] vs. 2.5 [1.3], P = 0.016). No association was detected between T2B and any of the histological measures. CONCLUSION: fB in MS WM is dependent on myelin content and may be a tool to monitor patients with this condition.},
  langid = {english}
}

@misc{Seiberlich2012-xi,
  title = {Nuclear Magnetic Resonance ({{NMR}}) Fingerprinting},
  author = {Seiberlich, N and Ma, D and Gulani, V and Griswold, M},
  year = {2012},
  month = sep,
  number = {20120235678 A1}
}

@book{Seiberlich2020-xe,
  title = {Quantitative Magnetic Resonance Imaging},
  author = {Seiberlich, Nicole and Gulani, Vikas and Campbell, Adrienne and Sourbron, Steven and Doneva, Mariya Ivanova and Calamante, Fernando and Hu, Houchun Harry},
  year = {2020},
  month = nov,
  publisher = {{Academic Press}},
  abstract = {Quantitative Magnetic Resonance Imaging is a `go-to' reference for methods and applications of quantitative magnetic resonance imaging, with specific sections on Relaxometry, Perfusion, and Diffusion. Each section will start with an explanation of the basic techniques for mapping the tissue property in question, including a description of the challenges that arise when using these basic approaches. For properties which can be measured in multiple ways, each of these basic methods will be described in separate chapters. Following the basics, a chapter in each section presents more advanced and recently proposed techniques for quantitative tissue property mapping, with a concluding chapter on clinical applications. The reader will learn: The basic physics behind tissue property mapping How to implement basic pulse sequences for the quantitative measurement of tissue properties The strengths and limitations to the basic and more rapid methods for mapping the magnetic relaxation properties T1, T2, and T2* The pros and cons for different approaches to mapping perfusion The methods of Diffusion-weighted imaging and how this approach can be used to generate diffusion tensor maps and more complex representations of diffusion How flow, magneto-electric tissue property, fat fraction, exchange, elastography, and temperature mapping are performed How fast imaging approaches including parallel imaging, compressed sensing, and Magnetic Resonance Fingerprinting can be used to accelerate or improve tissue property mapping schemes How tissue property mapping is used clinically in different organs Structured to cater for MRI researchers and graduate students with a wide variety of backgrounds Explains basic methods for quantitatively measuring tissue properties with MRI - including T1, T2, perfusion, diffusion, fat and iron fraction, elastography, flow, susceptibility - enabling the implementation of pulse sequences to perform measurements Shows the limitations of the techniques and explains the challenges to the clinical adoption of these traditional methods, presenting the latest research in rapid quantitative imaging which has the possibility to tackle these challenges Each section contains a chapter explaining the basics of novel ideas for quantitative mapping, such as compressed sensing and Magnetic Resonance Fingerprinting-based approaches},
  langid = {english}
}

@article{Seif2022-xg,
  title = {Reliability of Multi-Parameter Mapping ({{MPM}}) in the Cervical Cord: {{A}} Multi-Center Multi-Vendor Quantitative {{MRI}} Study},
  author = {Seif, Maryam and Leutritz, Tobias and Schading, Simon and Emmengger, Tim and Curt, Armin and Weiskopf, Nikolaus and Freund, Patrick},
  year = {2022},
  month = dec,
  journal = {NeuroImage},
  volume = {264},
  pages = {119751},
  doi = {10.1016/j.neuroimage.2022.119751},
  abstract = {MRI based multicenter studies which target neurological pathologies affecting the spinal cord and brain \textendash{} including spinal cord injury (SCI) \textendash{} require standardized acquisition protocols and image processing methods. We have optimized and applied a multi-parameter mapping (MPM) protocol that simultaneously covers the brain and the cervical cord within a traveling heads study across six clinical centers (Leutritz et al., 2020). The MPM protocol includes quantitative maps (magnetization transfer saturation (MT), proton density (PD), longitudinal (R1), and effective transverse (R2*) relaxation rates) sensitive to myelination, water content, iron concentration, and morphometric measures, such as cross-sectional cord area. Previously, we assessed the repeatability and reproducibility of the brain MPM data acquired in the five healthy participants who underwent two scan-rescans (Leutritz et al., 2020). This study focuses on the cervical cord MPM data derived from the same acquisitions to determine its repeatability and reproducibility in the cervical cord. MPM matrices of the cervical cord were generated and processed using the hMRI and the spinal cord toolbox. To determine reliability of the cervical MPM data, the intra-site (i.e., scan-rescan) coefficient of variation (CoV), inter-site CoV, and bias within region of interests (C1, C2 and C3 levels) were determined. The range of the mean intra- and inter-site CoV of MT, R1 and PD was between 2.5\% and 12\%, and between 1.1\% and 4.0\% for the morphometric measures. In conclusion, the cervical MPM data showed a high repeatability and reproducibility for key imaging biomarkers and hence can be employed as a standardized tool in multi-center studies, including clinical trials.}
}

@article{Seiler2020-na,
  title = {Cortical Aging - New Insights with Multiparametric Quantitative {{MRI}}},
  author = {Seiler, Alexander and Sch{\"o}ngrundner, Sophie and Stock, Benjamin and N{\"o}th, Ulrike and Hattingen, Elke and Steinmetz, Helmuth and Klein, Johannes C and Baudrexel, Simon and Wagner, Marlies and Deichmann, Ralf and Gracien, Ren{\'e}-Maxime},
  year = {2020},
  month = aug,
  journal = {Stress and The Aging Brain},
  volume = {12},
  number = {16},
  pages = {16195--16210},
  abstract = {Understanding the microstructural changes related to physiological aging of the cerebral cortex is pivotal to differentiate healthy aging from neurodegenerative processes. The aim of this study was to investigate the age-related global changes of cortical microstructure and regional patterns using multiparametric quantitative MRI (qMRI) in healthy subjects with a wide age range. 40 healthy participants (age range: 2nd to 8th decade) underwent high-resolution qMRI including T1, PD as well as T2, T2* and T2' mapping at 3 Tesla. Cortical reconstruction was performed with the FreeSurfer toolbox, followed by tests for correlations between qMRI parameters and age. Cortical T1 values were negatively correlated with age (p=0.007) and there was a widespread age-related decrease of cortical T1 involving the frontal and the parietotemporal cortex, while T2 was correlated positively with age, both in frontoparietal areas and globally (p=0.004). Cortical T2' values showed the most widespread associations across the cortex and strongest correlation with age (r= -0.724, p=0.0001). PD and T2* did not correlate with age. Multiparametric qMRI allows to characterize cortical aging, unveiling parameter-specific patterns. Quantitative T2' mapping seems to be a promising imaging biomarker of cortical age-related changes, suggesting that global cortical iron deposition is a prominent process in healthy aging.},
  langid = {english},
  doi = {10.18632/aging.103629}
}

@article{Smith2002-bn,
  title = {Fast Robust Automated Brain Extraction},
  author = {Smith, Stephen M},
  year = {2002},
  month = nov,
  journal = {Human Brain Mapping},
  volume = {17},
  number = {3},
  pages = {143--155},
  doi = {10.1002/hbm.10062},
  abstract = {An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against ``gold-standard'' hand segmentations, and two other popular automated methods.},
  langid = {english}
}

@article{Smith2004-oa,
  title = {Advances in Functional and Structural {{MR}} Image Analysis and Implementation as {{FSL}}},
  author = {Smith, Stephen M and Jenkinson, Mark and Woolrich, Mark W and Beckmann, Christian F and Behrens, Timothy E J and {Johansen-Berg}, Heidi and Bannister, Peter R and De Luca, Marilena and Drobnjak, Ivana and Flitney, David E and Niazy, Rami K and Saunders, James and Vickers, John and Zhang, Yongyue and De Stefano, Nicola and Brady, J Michael and Matthews, Paul M},
  year = {2004},
  journal = {Neuroimage},
  volume = {23 Suppl 1},
  pages = {S208--19},
  doi = {10.1016/j.neuroimage.2004.07.051},
  abstract = {The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).},
  langid = {english}
}

@article{Steen1995-se,
  title = {Age-Related Changes in Proton {{T1}} Values of Normal Human Brain},
  author = {Steen, R G and Gronemeyer, S A and Taylor, J S},
  year = {1995},
  journal = {Journal of Magnetic Resonance Imaging},
  volume = {5},
  number = {1},
  pages = {43--48},
  doi = {10.1002/jmri.1880050111},
  abstract = {To determine whether there were age-related changes in the brain tissue of 55 healthy adult volunteers (29 men, 26 women; 18-72 years old) without known brain abnormalities, a standard inversion-recovery technique was optimized for precise and accurate T1 measurement within the constraints of a 15-minute examination. Measurements of water proton T1 were obtained in eight brain regions. T1 increased with age in the genu (P \textexclamdown{} .001) (analysis of variance), frontal white matter (P \textexclamdown{} .05), occipital white matter (P \textexclamdown{} .05), putamen (P \textexclamdown{} .001), and thalamus (P \textexclamdown\textexclamdown{} .001). A significant decrease in T1 with age was found in cortical gray matter (P \textexclamdown{} .05). Thus, age-related changes in T1 are present in a healthy population, even if extremes of age are excluded, suggesting that T1 values generally increase with age. However, increases in T1 were also observed in the genu, putamen, and thalamus of a substantial fraction of volunteers less than 35 years old. Aging healthy persons can show subtle, nonsymptomatic brain changes, suggesting that brain aging is associated with occult processes that can begin at a relatively early age.},
  langid = {english}
}

@article{Stikov2015-gb,
  title = {On the Accuracy of {{T1}} Mapping: {{Searching}} for Common Ground},
  author = {Stikov, Nikola and Boudreau, Mathieu and Levesque, Ives R and Tardif, Christine L and Barral, Jo{\"e}lle K and Pike, G Bruce},
  year = {2015},
  month = feb,
  journal = {Magnetic Resonance in Medicine},
  volume = {73},
  number = {2},
  pages = {514--522},
  publisher = {{John Wiley \& Sons, Ltd}},
  doi = {10.1002/mrm.25135},
  abstract = {Purpose There are many T1 mapping methods available, each of them validated in phantoms and reporting excellent agreement with literature. However, values in literature vary greatly, with T1 in white matter ranging from 690 to 1100 ms at 3 Tesla. This brings into question the accuracy of one of the most fundamental measurements in quantitative MRI. Our goal was to explain these variations and look into ways of mitigating them. Theory and Methods We evaluated the three most common T1 mapping methods (inversion recovery, Look-Locker, and variable flip angle) through Bloch simulations, a white matter phantom and the brains of 10 healthy subjects (single-slice). We pooled the T1 histograms of the subjects to determine whether there is a sequence-dependent bias and whether it is reproducible across subjects. Results We found good agreement between the three methods in phantoms, but poor agreement in vivo, with the white matter T1 histogram peak in healthy subjects varying by more than 30\% depending on the method used. We also found that the pooled brain histograms displayed three distinct white matter peaks, with Look-Locker consistently underestimating, and variable flip angle overestimating the inversion recovery T1 values. The Bloch simulations indicated that incomplete spoiling and inaccurate B1 mapping could account for the observed differences. Conclusion We conclude that the three most common T1 mapping protocols produce stable T1 values in phantoms, but not in vivo. To improve the accuracy of T1 mapping, we recommend that sites perform in vivo validation of their T1 mapping method against the inversion recovery reference method, as the first step toward developing a robust calibration scheme. Magn Reson Med 73:514?522, 2015. ? 2014 Wiley Periodicals, Inc.},
  keywords = {accuracy,B1 mapping,inversion recovery,Look-Locker,precision,quantitative MRI,relaxometry,T1 mapping,variable flip angle}
}

@article{Stupic2021-lj,
  title = {A Standard System Phantom for Magnetic Resonance Imaging},
  author = {Stupic, Karl F and Ainslie, Maureen and Boss, Michael A and Charles, Cecil and Dienstfrey, Andrew M and Evelhoch, Jeffrey L and Finn, Paul and Gimbutas, Zydrunas and Gunter, Jeffrey L and Hill, Derek L G and Jack, Clifford R and Jackson, Edward F and Karaulanov, Todor and Keenan, Kathryn E and Liu, Guoying and Martin, Michele N and Prasad, Pottumarthi V and Rentz, Nikki S and Yuan, Chun and Russek, Stephen E},
  year = {2021},
  month = sep,
  journal = {Magnetic Resonance in Medicine},
  volume = {86},
  number = {3},
  pages = {1194--1211},
  doi = {10.1002/mrm.28779},
  abstract = {PURPOSE: A standard MRI system phantom has been designed and fabricated to assess scanner performance, stability, comparability and assess the accuracy of quantitative relaxation time imaging. The phantom is unique in having traceability to the International System of Units, a high level of precision, and monitoring by a national metrology institute. Here, we describe the phantom design, construction, imaging protocols, and measurement of geometric distortion, resolution, slice profile, signal-to-noise ratio (SNR), proton-spin relaxation times, image uniformity and proton density. METHODS: The system phantom, designed by the International Society of Magnetic Resonance in Medicine ad hoc committee on Standards for Quantitative MR, is a 200 mm spherical structure that contains a 57-element fiducial array; two relaxation time arrays; a proton density/SNR array; resolution and slice-profile insets. Standard imaging protocols are presented, which provide rapid assessment of geometric distortion, image uniformity, T1 and T2 mapping, image resolution, slice profile, and SNR. RESULTS: Fiducial array analysis gives assessment of intrinsic geometric distortions, which can vary considerably between scanners and correction techniques. This analysis also measures scanner/coil image uniformity, spatial calibration accuracy, and local volume distortion. An advanced resolution analysis gives both scanner and protocol contributions. SNR analysis gives both temporal and spatial contributions. CONCLUSIONS: A standard system phantom is useful for characterization of scanner performance, monitoring a scanner over time, and to compare different scanners. This type of calibration structure is useful for quality assurance, benchmarking quantitative MRI protocols, and to transition MRI from a qualitative imaging technique to a precise metrology with documented accuracy and uncertainty.},
  langid = {english},
  keywords = {MRI standards,phantom,quality assurance,quantitative MRI}
}

@article{Theaud2020-mu,
  title = {{{TractoFlow}}: {{A}} Robust, Efficient and Reproducible Diffusion {{MRI}} Pipeline Leveraging {{Nextflow}} \& {{Singularity}}},
  author = {Theaud, Guillaume and Houde, Jean-Christophe and Bor{\'e}, Arnaud and Rheault, Fran{\c c}ois and Morency, Felix and Descoteaux, Maxime},
  year = {2020},
  month = sep,
  journal = {Neuroimage},
  volume = {218},
  pages = {116889},
  doi = {10.1016/j.neuroimage.2020.116889},
  abstract = {Diffusion MRI tractography processing pipeline requires a large number of steps (typically 20+ steps). If parameters of these steps, number of threads, and random seed generators are not carefully controlled, the resulting tractography can easily be non-reproducible and non-replicable, even in test-test experiments. To handle these issues, we developed TractoFlow. TractoFlow is fully automatic from raw diffusion weighted images to tractography. The pipeline also outputs classical diffusion tensor imaging measures and several fiber orientation distribution function measures. TractoFlow supports the recent Brain Imaging Data Structure (BIDS) format as input and is based on two engines: Nextflow and Singularity. In this work, the TractoFlow pipeline is evaluated on three databases and shown to be efficient and reproducible from 98\% to 100\%, depending on parameter choices. Moreover, it is easy to use for non-technical users, with little to no installation requirements. TractoFlow is publicly available for academic research and is an important step forward for better structural brain connectivity mapping.},
  langid = {english}
}

@inproceedings{Theaud2022-gu,
  title = {{{dMRIQCpy}}: A Python-Based Toolbox for Diffusion {{MRI}} Quality Control and Beyond},
  booktitle = {International {{Society}} for {{Magnetic Resonance}} in {{Medicine}} ({{ISMRM}}) {{Annual Meeting}}},
  author = {Theaud, Guillaume and Descoteaux, Maxime},
  year = {2022},
  pages = {3906},
  address = {{London}},
  doi = {10.58530/2022/3906}
}

@article{Tofts1998-kr,
  title = {Standardisation and Optimisation of Magnetic Resonance Techniques for Multicentre Studies},
  author = {Tofts, P S},
  year = {1998},
  month = may,
  journal = {Journal of Neurology, Neurosurgery and Psychiatry},
  volume = {64 Suppl 1},
  pages = {S37--43},
  abstract = {AIM: An approach to measuring physical quantities such as lesion load with MRI in multicentre studies is presented. METHOD: Examples are given of imperfections in current techniques: (1) a step change in a serial trial, giving an apparent (but artefactual) decrease in total lesion volume in untreated patients with multiple sclerosis; (2) inaccuracy (systematic error) in lesion volume, found by measuring a phantom with lesions of known volumes; (3) spatial non-uniformity in the radiofrequency coil sensitivity, giving gross image shading. When using the magnetic resonance imaging (MRI) scanner as a scientific instrument to measure physical quantities, accuracy (closeness to the truth, or lack of systematic error), and precision (reproducibility, or lack of random error) are the keys to success. Quality assurance procedures can utilise phantoms, normal control subjects, and stable patients, and have to be included in serial studies and trials. Between scanner agreement can perhaps be improved by attempting to replicate an inaccurate procedure at each site; but it is more preferable to seek accuracy and precision (as a perfectly accurate and precise procedure must give the same results at all sites). CONCLUSION: Before being included in a serial study, a measurement procedure should ideally demonstrate:(1) accuracy in a phantom; (2) precision in repeated measurements on a phantom; (3) precision in repeated measurements on human subjects.},
  langid = {english}
}

@article{Tournier2019-gb,
  title = {{{MRtrix3}}: {{A}} Fast, Flexible and Open Software Framework for Medical Image Processing and Visualisation},
  author = {Tournier, J-Donald and Smith, Robert and Raffelt, David and Tabbara, Rami and Dhollander, Thijs and Pietsch, Maximilian and Christiaens, Daan and Jeurissen, Ben and Yeh, Chun-Hung and Connelly, Alan},
  year = {2019},
  month = nov,
  journal = {Neuroimage},
  volume = {202},
  pages = {116137},
  abstract = {MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualisation, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.},
  langid = {english},
  doi = {10.1016/j.neuroimage.2019.116137}
}

@article{Ullmann2014-pn,
  title = {Automatic Labeling of Vertebral Levels Using a Robust Template-Based Approach},
  author = {Ullmann, Eug{\'e}nie and Pelletier Paquette, Jean Fran{\c c}ois and Thong, William E and {Cohen-Adad}, Julien},
  year = {2014},
  month = jul,
  journal = {International Journal of Biomedical Imaging},
  volume = {2014},
  pages = {719520},
  abstract = {Context. MRI of the spinal cord provides a variety of biomarkers sensitive to white matter integrity and neuronal function. Current processing methods are based on manual labeling of vertebral levels, which is time consuming and prone to user bias. Although several methods for automatic labeling have been published; they are not robust towards image contrast or towards susceptibility-related artifacts. Methods. Intervertebral disks are detected from the 3D analysis of the intensity profile along the spine. The robustness of the disk detection is improved by using a template of vertebral distance, which was generated from a training dataset. The developed method has been validated using T1- and T2-weighted contrasts in ten healthy subjects and one patient with spinal cord injury. Results. Accuracy of vertebral labeling was 100\%. Mean absolute error was 2.1 {$\pm$} 1.7 mm for T2-weighted images and 2.3 {$\pm$} 1.6 mm for T1-weighted images. The vertebrae of the spinal cord injured patient were correctly labeled, despite the presence of artifacts caused by metallic implants. Discussion. We proposed a template-based method for robust labeling of vertebral levels along the whole spinal cord for T1- and T2-weighted contrasts. The method is freely available as part of the spinal cord toolbox.},
  langid = {english},
  doi = {10.1155/2014/719520}
}

@article{Voelker2021-vi,
  title = {The Traveling Heads 2.0: {{Multicenter}} Reproducibility of Quantitative Imaging Methods at 7 {{Tesla}}},
  author = {Voelker, Maximilian N and Kraff, Oliver and Goerke, Steffen and Laun, Frederik B and Hanspach, Jannis and Pine, Kerrin J and Ehses, Philipp and Zaiss, Moritz and Liebert, Andrzej and Straub, Sina and Eckstein, Korbinian and Robinson, Simon and Nagel, Armin N and Stefanescu, Maria R and Wollrab, Astrid and Klix, Sabrina and Felder, J{\"o}rg and Hock, Michael and Bosch, Dario and Weiskopf, Nikolaus and Speck, Oliver and Ladd, Mark E and Quick, Harald H},
  year = {2021},
  month = may,
  journal = {Neuroimage},
  volume = {232},
  pages = {117910},
  abstract = {OBJECT: This study evaluates inter-site and intra-site reproducibility at ten different 7 T sites for quantitative brain imaging. MATERIAL AND METHODS: Two subjects - termed the ``traveling heads'' - were imaged at ten different 7 T sites with a harmonized quantitative brain MR imaging protocol. In conjunction with the system calibration, MP2RAGE, QSM, CEST and multi-parametric mapping/relaxometry were examined. RESULTS: Quantitative measurements with MP2RAGE showed very high reproducibility across sites and subjects, and errors were in concordance with previous results and other field strengths. QSM had high inter-site reproducibility for relevant subcortical volumes. CEST imaging revealed systematic differences between the sites, but reproducibility was comparable to results in the literature. Relaxometry had also very high agreement between sites, but due to the high sensitivity, differences caused by different applications of the B1 calibration of the two RF coil types used were observed. CONCLUSION: Our results show that quantitative brain imaging can be performed with high reproducibility at 7 T and with similar reliability as found at 3 T for multicenter studies of the supratentorial brain.},
  langid = {english},
  doi = {10.1016/j.neuroimage.2021.117910}
}

@article{Wang2017-ah,
  title = {Clinical Quantitative Susceptibility Mapping ({{QSM}}): {{Biometal}} Imaging and Its Emerging Roles in Patient Care},
  author = {Wang, Yi and Spincemaille, Pascal and Liu, Zhe and Dimov, Alexey and Deh, Kofi and Li, Jianqi and Zhang, Yan and Yao, Yihao and Gillen, Kelly M and Wilman, Alan H and Gupta, Ajay and Tsiouris, Apostolos John and Kovanlikaya, Ilhami and Chiang, Gloria Chia-Yi and Weinsaft, Jonathan W and Tanenbaum, Lawrence and Chen, Weiwei and Zhu, Wenzhen and Chang, Shixin and Lou, Min and Kopell, Brian H and Kaplitt, Michael G and Devos, David and Hirai, Toshinori and Huang, Xuemei and Korogi, Yukunori and Shtilbans, Alexander and Jahng, Geon-Ho and Pelletier, Daniel and Gauthier, Susan A and Pitt, David and Bush, Ashley I and Brittenham, Gary M and Prince, Martin R},
  year = {2017},
  month = oct,
  journal = {Journal of Magnetic Resonance Imaging},
  volume = {46},
  number = {4},
  pages = {951--971},
  doi = {10.1002/jmri.25693},
  abstract = {UNLABELLED: Quantitative susceptibility mapping (QSM) has enabled magnetic resonance imaging (MRI) of tissue magnetic susceptibility to advance from simple qualitative detection of hypointense blooming artifacts to precise quantitative measurement of spatial biodistributions. QSM technology may be regarded to be sufficiently developed and validated to warrant wide dissemination for clinical applications of imaging isotropic susceptibility, which is dominated by metals in tissue, including iron and calcium. These biometals are highly regulated as vital participants in normal cellular biochemistry, and their dysregulations are manifested in a variety of pathologic processes. Therefore, QSM can be used to assess important tissue functions and disease. To facilitate QSM clinical translation, this review aims to organize pertinent information for implementing a robust automated QSM technique in routine MRI practice and to summarize available knowledge on diseases for which QSM can be used to improve patient care. In brief, QSM can be generated with postprocessing whenever gradient echo MRI is performed. QSM can be useful for diseases that involve neurodegeneration, inflammation, hemorrhage, abnormal oxygen consumption, substantial alterations in highly paramagnetic cellular iron, bone mineralization, or pathologic calcification; and for all disorders in which MRI diagnosis or surveillance requires contrast agent injection. Clinicians may consider integrating QSM into their routine imaging practices by including gradient echo sequences in all relevant MRI protocols. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:951-971.},
  langid = {english},
  keywords = {biometals,quantitative susceptibility mapping}
}

@article{Wang2017-yt,
  title = {Stability of {{R2}}* and Quantitative Susceptibility Mapping of the Brain Tissue in a Large Scale Multi-Center Study},
  author = {Wang, Rongpin and Xie, Guangyou and Zhai, Maoxiong and Zhang, Zhongping and Wu, Bing and Zheng, Dandan and Hong, Nan and Jiang, Tao and Wen, Baohong and Cheng, Jingliang},
  year = {2017},
  month = mar,
  journal = {Scientific Reports},
  volume = {7},
  pages = {45261},
  doi = {10.1038/srep45261},
  abstract = {Multi-center studies are advantageous for enrolling participants of varying pathological and demographical conditions, and especially in neurological studies. Hence stability of the obtained quantitative R2* and susceptibility in multicenter studies is a key issue for their widespread applications. In this work, the stabilities of simultaneously obtained R2* and susceptibility are investigated and compared across 10 sites that are equipped with the same scanner and receiver coil, the same post-processing process was used to achieve consistent experiment setup. Two healthy adult volunteers (one male and female) participated in this study. High intraclass correlation coefficient was obtained for both susceptibility (0.94) and R2* (0.96). The coefficients of variance for all measurements obtained were smaller than 0.1, the largest variations of measurements in all the chosen ROIs fall within {$\pm$}20\% from the median value. Higher level of stability was obtained in R2* as compared to susceptibility at 1 mm resolution (P \textexclamdown{} 0.05) and at 1.5 mm (P \textexclamdown{} 0.01).},
  langid = {english}
}

@article{York2022-fl,
  title = {Longitudinal Microstructural {{MRI}} Markers of Demyelination and Neurodegeneration in Early Relapsing-Remitting Multiple Sclerosis: {{Magnetisation}} Transfer, Water Diffusion and g-Ratio},
  author = {York, Elizabeth N and Meijboom, Rozanna and Thrippleton, Michael J and Bastin, Mark E and Kampaite, Agniete and White, Nicole and Chandran, Siddharthan and Waldman, Adam D},
  year = {2022},
  month = jan,
  journal = {NeuroImage: Clinical},
  volume = {36},
  pages = {103228},
  doi = {10.1016/j.nicl.2022.103228},
  abstract = {Introduction Quantitative microstructural MRI, such as myelin-sensitive magnetisation transfer ratio (MTR) or saturation (MTsat), axon-sensitive water diffusion Neurite Orientation Dispersion and Density Imaging (NODDI), and the aggregate g-ratio, may provide more specific markers of white matter integrity than conventional MRI for early patient stratification in relapsing-remitting multiple sclerosis (RRMS). The aim of this study was to determine the sensitivity of such markers to longitudinal pathological change within cerebral white matter lesions (WML) and normal-appearing white matter (NAWM) in recently diagnosed RRMS. Methods Seventy-nine people with recently diagnosed RRMS, from the FutureMS longitudinal cohort, were recruited to an extended MRI protocol at baseline and one year later. Twelve healthy volunteers received the same MRI protocol, repeated within two weeks. Ethics approval and written informed consent were obtained. 3T MRI included magnetisation transfer, and multi-shell diffusion-weighted imaging. NAWM and whole brain were segmented from 3D T1-weighted MPRAGE, and WML from T2-weighted FLAIR. MTR, MTsat, NODDI isotropic (ISOVF) and intracellular (ICVF) volume fractions, and g-ratio (calculated from MTsat and NODDI data) were measured within WML and NAWM. Brain parenchymal fraction (BPF) was also calculated. Longitudinal change in BPF and microstructural metrics was assessed with paired t-tests ({$\alpha$} = 0.05) and linear mixed models, adjusted for confounding factors with False Discovery Rate (FDR) correction for multiple comparisons. Longitudinal changes were compared with test-retest Bland-Altman limits of agreement from healthy control white matter. The influence of longitudinal change on g-ratio was explored through post-hoc analysis in silico by computing g-ratio with realistic simulated MTsat and NODDI values. Results In NAWM, g-ratio and ICVF increased, and MTsat decreased over one year (adjusted mean difference = 0.007, 0.005, and -0.057 respectively, all FDR-corrected p \textexclamdown{} 0.05). There was no significant change in MTR, ISOVF, or BPF. In WML, MTsat, NODDI ICVF and ISOVF increased over time (adjusted mean difference = 0.083, 0.024 and 0.016, respectively, all FDR-corrected p \textexclamdown{} 0.05). Group-level longitudinal changes exceeded test-retest limits of agreement for NODDI ISOVF and ICVF in WML only. In silico analysis showed g-ratio may increase due to a decrease in MTsat or ISOVF, or an increase in ICVF. Discussion G-ratio and MTsat changes in NAWM over one year may indicate subtle myelin loss in early RRMS, which were not apparent with BPF or NAWM MTR. Increases in NAWM and WML NODDI ICVF were not anticipated, and raise the possibility of axonal swelling or morphological change. Increases in WML MTsat may reflect myelin repair. Changes in NODDI ISOVF are more likely to reflect alterations in water content. Competing MTsat and ICVF changes may account for the absence of g-ratio change in WML. Longitudinal changes in microstructural measures are significant at a group level, however detection in individual patients in early RRMS is limited by technique reproducibility. Conclusion MTsat and g-ratio are more sensitive than MTR to early pathological changes in RRMS, but complex dependence of g-ratio on NODDI parameters limit the interpretation of aggregate measures in isolation. Improvements in technique reproducibility and validation of MRI biophysical models across a range of pathological tissue states are needed.}
}

@article{Zhang2001-rn,
  title = {Segmentation of Brain {{MR}} Images through a Hidden {{Markov}} Random Field Model and the Expectation-Maximization Algorithm},
  author = {Zhang, Y and Brady, M and Smith, S},
  year = {2001},
  month = jan,
  journal = {IEEE Transactions on Medical Imaging},
  volume = {20},
  number = {1},
  pages = {45--57},
  doi = {10.1109/42.906424},
  abstract = {The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation\textendash no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.},
  langid = {english}
}

@misc{Karakuzu2022-nlwf,
 title={NeuroLibre : A preprint server for full-fledged reproducible neuroscience},
 url={osf.io/h89js},
 DOI={10.31219/osf.io/h89js},
 publisher={OSF Preprints},
 author={Karakuzu, Agah and DuPre, Elizabeth and Tetrel, Loic and Bermudez, Patrick and Boudreau, Mathieu and Chin, Mary and Poline, Jean-Baptiste and Das, Samir and Bellec, Pierre and Stikov, Nikola},
 year={2022},
 month={Apr}
}

@article{Dupre2022-iro,
  title={Beyond advertising: New infrastructures for publishing integrated research objects},
  author={DuPre, Elizabeth and Holdgraf, Chris and Karakuzu, Agah and Tetrel, Lo{\"\i}c and Bellec, Pierre and Stikov, Nikola and Poline, Jean-Baptiste},
  journal={PLOS Computational Biology},
  doi = {10.1371/journal.pcbi.1009651},
  volume={18},
  number={1},
  pages={e1009651},
  year={2022},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{Harding2023-conp,
	title = {The {Canadian} {Open} {Neuroscience} {Platform}—{An} open science framework for the neuroscience community},
	volume = {19},
	url = {10.1371/journal.pcbi.1011230},
	doi = {10.1371/journal.pcbi.1011230},
	abstract = {The Canadian Open Neuroscience Platform (CONP) takes a multifaceted approach to enabling open neuroscience, aiming to make research, data, and tools accessible to everyone, with the ultimate objective of accelerating discovery. Its core infrastructure is the CONP Portal, a repository with a decentralized design, where datasets and analysis tools across disparate platforms can be browsed, searched, accessed, and shared in accordance with FAIR principles. Another key piece of CONP infrastructure is NeuroLibre, a preprint server capable of creating and hosting executable and fully reproducible scientific publications that embed text, figures, and code. As part of its holistic approach, the CONP has also constructed frameworks and guidance for ethics and data governance, provided support and developed resources to help train the next generation of neuroscientists, and has fostered and grown an engaged community through outreach and communications. In this manuscript, we provide a high-level overview of this multipronged platform and its vision of lowering the barriers to the practice of open neuroscience and yielding the associated benefits for both individual researchers and the wider community.},
	number = {7},
	journal = {PLOS Computational Biology},
	author = {Harding, Rachel J. and Bermudez, Patrick and Bernier, Alexander and Beauvais, Michael and Bellec, Pierre and Hill, Sean and Karakuzu, Agah and Knoppers, Bartha M. and Pavlidis, Paul and Poline, Jean-Baptiste and Roskams, Jane and Stikov, Nikola and Stone, Jessica and Strother, Stephen and Consortium, CONP and Evans, Alan C.},
	month = jul,
	year = {2023},
	note = {Publisher: Public Library of Science},
	pages = {1--14},
}