+ Summary
+ Spinal cord morphometry measures derived from magnetic resonance
+ imaging (MRI) scans serve as valuable prognostic biomarkers for
+ various spinal cord pathologies. Despite their significance,
+ interpreting these biomarkers is challenging due to substantial
+ variability between subjects. The lack of a standardized normalization
+ method to mitigate this variability and the need for a better
+ understanding of morphometric distribution contribute to the current
+ knowledge gap.
+ In this work, we present a database of healthy normative values for
+ six commonly used measures of spinal cord morphometry built using a
+ new fully-automatic normalization approach. Morphometric measures were
+ computed from a large open-access dataset of healthy adult volunteers
+ (N = 203) and brought to the common space of the PAM50 spinal cord
+ template using a newly proposed normalization method based on linear
+ interpolation
+ [fig:figure1].
+ The database is interactive, available online
+ (https://preprint.neurolibre.org/10.55458/neurolibre.00017)
+ and allows filtering for sex, age, and MRI vendors. The proposed
+ method is open-source and easily accessible through the Spinal Cord
+ Toolbox (SCT) v6.0 and higher.
+ This new morphometric database allows researchers to normalize
+ morphometrics based on sex and age, thereby minimizing inter-subject
+ variability associated with demographic and biological factors.
+
+
+ Acknowledgements
+ We thank Nick Guenther and Mathieu Guay-Paquet for their assistance
+ with dataset management, Joshua Newton for his help with implementing
+ the algorithm in SCT, and Allan R. Martin for his insightful
+ discussions on the clinical aspects of the work. We also thank Nathan
+ Molinier for providing valuable feedback on the manuscript figures. We
+ acknowledge all participants as well as collaborators of the
+ spine-generic study
+ (https://spine-generic.readthedocs.io).
+ Funded by the Canada Research Chair in Quantitative Magnetic
+ Resonance Imaging [CRC-2020-00179], the Canadian Institute of Health
+ Research [PJT-190258], the Canada Foundation for Innovation [32454,
+ 34824], the Fonds de Recherche du Québec - Santé [322736], the Natural
+ Sciences and Engineering Research Council of Canada
+ [RGPIN-2019-07244], the Canada First Research Excellence Fund (IVADO
+ and TransMedTech), the Courtois NeuroMod project, the Quebec
+ BioImaging Network [5886, 35450], INSPIRED (Spinal Research, UK; Wings
+ for Life, Austria; Craig H. Neilsen Foundation, USA), Mila - Tech
+ Transfer Funding Program. Supported by the Ministry of Health of the
+ Czech Republic, grant nr. NU22-04-00024. All rights reserved. JV has
+ received funding from the European Union’s Horizon Europe research and
+ innovation programme under the Marie Sktodowska-Curie grant agreement
+ No 101107932.
+
+
+ NOTE: The following section in this
+ document repeats the narrative content exactly as found in the
+ corresponding
+ NeuroLibre Reproducible Preprint (NRP). The content was
+ automatically incorporated into this PDF using the NeuroLibre
+ publication workflow
+ (Karakuzu,
+ DuPre, et al., 2022) to credit the referenced resources. The
+ submitting author of the preprint has verified and approved the
+ inclusion of this section through a GitHub pull request made to the
+ source
+ repository from which this document was built. Please
+ note that the figures and tables have been excluded from this
+ (static) document. To interactively explore such outputs and
+ re-generate them, please visit the corresponding
+ NRP.
+ For more information on integrated research objects (e.g., NRPs)
+ that bundle narrative and executable content for reproducible and
+ transparent publications, please refer to DuPre et al.
+ (2022).
+ NeuroLibre is sponsored by the Canadian Open Neuroscience Platform
+ (CONP)
+ (Harding
+ et al., 2023).
+
+
+
+ 1. INTRODUCTION
+
+ 1.1 Spinal cord morphometry measures
+ The spinal cord plays a vital role in the central nervous system
+ by transmitting sensory and motor signals between the brain and the
+ rest of the body. It also contains essential networks responsible
+ for functions such as locomotion and pain processing. Structural
+ magnetic resonance imaging (MRI) is commonly used to assess spinal
+ cord macrostructure and to compute measures of spinal cord
+ morphometry like cross-sectional area (CSA) or anteroposterior (AP)
+ diameter. The morphometric measures serve as objective indicators to
+ evaluate spinal cord pathologies, such as the extent of spinal cord
+ atrophy in multiple sclerosis
+ (Losseff
+ et al., 1996;
+ Mina
+ et al., 2021;
+ Rocca
+ et al., 2019) and amyotrophic lateral sclerosis
+ (El
+ Mendili et al., 2023;
+ Paquin
+ et al., 2018) or the severity of spinal cord injury and
+ spinal cord compression in traumatic and non-traumatic spinal cord
+ injury, respectively
+ (Badhiwala
+ et al., 2020;
+ David
+ et al., 2019;
+ Miyanji
+ et al., 2007).
+ However, interpreting morphometric measures is challenging due to
+ considerable inter-subject variability associated with demographic
+ and biological factors. For example, significantly smaller CSA is
+ consistently reported in females relative to males
+ (Bédard
+ & Cohen-Adad, 2022;
+ Engl
+ et al., 2013;
+ Mina
+ et al., 2021;
+ Papinutto
+ et al., 2015,
+ 2020;
+ Rashid
+ et al., 2006;
+ Solstrand
+ Dahlberg et al., 2020;
+ Yanase
+ et al., 2006). Similarly, studies showed an association of
+ spinal cord CSA with cervical cord length
+ (Martin
+ et al., 2017a,
+ 2017b;
+ Oh
+ et al., 2014), spinal canal area, and spinal canal diameters
+ (Kesenheimer
+ et al., 2021;
+ Papinutto
+ et al., 2020). Other factors, such as brain volume,
+ intracranial volume, and thalamic volume also showed a strong
+ correlation with spinal cord CSA
+ (Bédard
+ & Cohen-Adad, 2022;
+ Papinutto
+ et al., 2020;
+ Rashid
+ et al., 2006;
+ Solstrand
+ Dahlberg et al., 2020).
+ As for weight and height, studies showed only a moderate
+ correlation with spinal cord CSA
+ (Papinutto
+ et al., 2020;
+ Yanase
+ et al., 2006) or did not show any significant association
+ (Bédard
+ & Cohen-Adad, 2022;
+ Papinutto
+ et al., 2020;
+ Solstrand
+ Dahlberg et al., 2020). Likewise, only a weak non-significant
+ association was reported between spinal cord CSA and age
+ (Bédard
+ & Cohen-Adad, 2022;
+ Kato
+ et al., 2012;
+ Papinutto
+ et al., 2020;
+ Yanase
+ et al., 2006). A single study with a wide cohort age range
+ reported that CSA increases until about 45 years of age and then
+ begins to decrease
+ (Papinutto
+ et al., 2020).
+ In addition to inter-subject variability, spinal cord anatomy
+ varies depending on the level. Corresponding with anatomical
+ textbooks
+ (Standring,
+ 2020), studies have shown an increase in CSA around vertebral
+ levels C4-C5 corresponding to cervical enlargement
+ (De
+ Leener et al., 2018;
+ Frostell
+ et al., 2016;
+ Horáková
+ et al., 2022;
+ Martin
+ et al., 2017b;
+ Mina
+ et al., 2021;
+ Rocca
+ et al., 2019). Then, the spinal cord cross-section becomes
+ smaller, which is mirrored by the decrease in CSA.
+
+
+ 1.2 Normalization strategies
+ Various normalization strategies have been proposed to account
+ for the above-mentioned factors on spinal cord morphometric
+ measures. Sex was used for CSA normalization in several works
+ (Bédard
+ & Cohen-Adad, 2022;
+ Kesenheimer
+ et al., 2021;
+ Papinutto
+ et al., 2020;
+ Rashid
+ et al., 2006). Other studies proposed spinal cord length as a
+ normalization factor
+ (El
+ Mendili et al., 2023;
+ Martin
+ et al., 2017a,
+ 2017b;
+ Oh
+ et al., 2014;
+ Rocca
+ et al., 2019). Additionally, combining spinal cord length
+ with a z-score normalization was proposed to account for variations
+ along the superior-inferior axis
+ (Martin
+ et al., 2017a,
+ 2017b).
+ Another approach taking into account the dependency of spinal cord
+ anatomy on a level involved the normalization of morphometric
+ measures from the compression site using non-compressed levels above
+ and below
+ (Guo
+ et al., 2022;
+ Miyanji
+ et al., 2007). Finally, several studies normalized CSA using
+ the spinal canal and brain metrics, including spinal canal area,
+ spinal canal diameter, brain volume, intracranial volume, thalamic
+ volume, and head size normalization factor
+ (Bédard
+ & Cohen-Adad, 2022;
+ Horsfield
+ et al., 2010;
+ Kesenheimer
+ et al., 2021;
+ Papinutto
+ et al., 2020;
+ Rashid
+ et al., 2006;
+ Rocca
+ et al., 2019).
+ While normalization strategies showed promising outcomes, there
+ is currently no accepted consensus on which method to use
+ (Cohen-Adad
+ et al., 2021a;
+ Papinutto
+ et al., 2020), and their practical implementation may
+ encounter several challenges. First, measuring the spinal cord
+ length and the spinal canal area can be time-consuming due to the
+ absence of reliable automatic measurement techniques. Second,
+ obtaining brain MRI scans, necessary for assessing brain and
+ thalamic volumes, may not be routinely available in spinal cord MRI
+ protocols, and neurodegenerative diseases such as multiple sclerosis
+ can influence brain measurements and potentially introduce bias
+ during normalization.
+
+
+ 1.3 Spinal cord template
+ Similarly to brain studies, spinal cord studies involving
+ multiple subjects frequently rely on templates — standardized,
+ high-resolution images of the human spinal cord used as a reference
+ for comparing and analyzing individual spinal cord scans. A commonly
+ used spinal cord template is the PAM50
+ (De
+ Leener et al., 2018). The process of aligning individual
+ single-subject images to the template typically involves a series of
+ non-linear image transformations, which may introduce inaccuracies
+ when computing morphometric measures in the PAM50 vs. in the native
+ subject’s space. This is an important consideration, especially in
+ subjects with altered spinal cord anatomy, such as patients with
+ spinal cord injury.
+
+
+ 1.4 Normative values
+ Several multi-subject studies have provided normative values for
+ spinal cord morphometry
+ (De
+ Leener et al., 2018;
+ Frostell
+ et al., 2016;
+ Horáková
+ et al., 2022;
+ Kato
+ et al., 2012;
+ Taso
+ et al., 2016). However, these studies show inconsistency in
+ their reporting. Some authors only provided values for
+ intervertebral discs
+ (De
+ Leener et al., 2018;
+ Horáková
+ et al., 2022), while others presented values averaged across
+ multiple vertebral levels
+ (Taso
+ et al., 2016). Notably, none of these studies have presented
+ normative values separated by sex.
+
+
+ 1.5 Study Objective
+ In this study, we present a database of healthy normative values
+ for six commonly used measures of spinal cord morphometry built
+ using a new fully-automatic normalization approach. The database is
+ interactive, available
+ online
+ and allows filtering by sex, age, and MRI vendors. The proposed
+ methodology is open-source, easily accessible through the Spinal
+ Cord Toolbox (SCT)
+ (De
+ Leener et al., 2017), and can be used in future multi-subject
+ studies to minimize inter- and intra-subject variability.
+
+
+
+ 2. MATERIALS AND METHODS
+
+ 2.1 Participants
+ We used data from the spine-generic
+ multi-subject dataset
+ (Cohen-Adad
+ et al., 2021b). The dataset is open-access, organized
+ according to the Brain Imaging Data Structure (BIDS) standard
+ (Gorgolewski
+ et al., 2016;
+ Karakuzu,
+ Appelhoff, et al., 2022) and managed using
+ git-annex
+ in
+ this
+ GitHub repository.
+ Participants were scanned across 43 centers on 3T MRI scanners
+ from 3 vendors (GE, Philips and Siemens) using the consensus
+ spine-generic acquisition protocol
+ (Cohen-Adad
+ et al., 2021a). For details on sequence parameters, see
+ Cohen-Adad et al.
+ (2021a);
+ Cohen-Adad et al.
+ (2021b).
+ Two experienced radiologists (MK, TR) evaluated MRI scans with a
+ focus on the presence of spinal cord compression. Spinal cord
+ compression was defined as a change in spinal cord contour at the
+ level of an intervertebral disc on an axial or sagittal MRI plane
+ compared with that at the midpoint level of neighbouring vertebrae
+ (Kadanka
+ et al., 2017;
+ Keřkovský
+ et al., 2017). Minor abnormalities such as mild disc
+ protrusions, spine misalignment or minimal widening of the spinal
+ cord central canal were not considered significant pathologies.
+ Qualitative assessment of the spine-generic
+ dataset by two experienced radiologists revealed mostly mild spinal
+ cord compression in 64 out of the total 267 volunteers (see the
+ “pathology” column in
+ this
+ spreadsheet). Those volunteers were excluded from the
+ further analysis. The final cohort used for the normative database
+ construction consisted of 203 healthy subjects (105 males and 98
+ females). Detailed demographic characteristics are provided in Table
+ 1.
+
+
+ 2.2 Data pre-processing
+ We used the T2-weighted images (0.8 mm isotropic resolution)
+ covering at least C1 to T1 vertebral levels. The image processing
+ was performed automatically using SCT v6.0
+ (De
+ Leener et al., 2017). For each participant, the spinal cord
+ was segmented using a deep learning-based algorithm
+ (Gros
+ et al., 2019) and the intervertebral discs were labeled
+ (Ullmann
+ et al., 2014) to generate the cord segmentation labeled with
+ vertebral levels (Figure 1A).
+ The spinal cord segmentation and disc labels were visually
+ inspected using SCT’s quality control report
+ (sct_qc function) and manually corrected when
+ necessary. The manual corrections ensured that the spinal cord
+ segmentation masks used for the computation of morphometric measures
+ were reliable. Segmentation masks were corrected using FSLeyes image
+ viewer
+ (McCarthy,
+ 2022) by adding or removing voxels when appropriate.
+ Regarding vertebral labeling corrections, we manually identified the
+ posterior tip of the intervertebral discs using SCT’s
+ sct_label_utils function when it was
+ necessary.
+
+
+ 2.3 Normalization
+ Figure 1 shows a schematic representation of the fully-automatic
+ normalization approach based on linear interpolation of morphometric
+ measures from the subject’s native space to the anatomical
+ dimensions of the PAM50 spinal cord template
+ (De
+ Leener et al., 2018).
+ After the preprocessing (i.e., spinal cord segmentation and
+ labeling), the morphometric measures were computed across individual
+ axial slices from the spinal cord segmentation mask in the subject’s
+ native space. Then, the number of axial slices corresponding to each
+ vertebral level was identified in both the subject’s native space
+ and in the PAM50 template based on the labeled segmentation.
+ Finally, the computed morphometric measures were linearly
+ interpolated to the PAM50 anatomical dimensions based on the number
+ of slices for each vertebral level in the native space and the PAM50
+ template. The following morphometric measures were computed using
+ SCT’s sct_process_segmentation for each
+ participant: cross-sectional area (CSA), anteroposterior (AP)
+ diameter, transverse diameter, compression ratio, eccentricity, and
+ solidity.
+ Spinal cord CSA reflects the atrophy of the spinal cord and is
+ computed as the area of the spinal cord in the transverse plane. The
+ AP diameter is the measurement of the diameter of the spinal cord in
+ the anterior-posterior direction, while the transverse diameter is
+ the measurement of the diameter of the spinal cord from side to
+ side. The compression ratio reflects the flattening of the spinal
+ cord and is defined as the ratio of the AP diameter and the
+ transverse diameter. Eccentricity is defined as the ratio of the
+ focal distance over the major axis length of an ellipse with the
+ same second moments as the spinal cord. The value is in the interval
+ [0, 1]. When it is 0, the ellipse becomes a
+ circle. Solidity is used to measure the indentation of the spinal
+ cord and is defined as the ratio of the area representing the spinal
+ cord to the area of the smallest convex polygon surrounding all
+ positive pixels in the image. Solidity is relevant in detecting
+ non-convex shapes, for instance, in subjects with spinal cord
+ compression.
+
+
+ 2.4 Normative values and interactive database
+ Morphometric measures normalized to the PAM50 space were used for
+ the calculation of normative values of the spinal cord morphometry.
+ The normative values were calculated as mean and standard deviation
+ across participants for slices in PAM50 space corresponding to each
+ intervertebral disc and the middle of each vertebral level. The
+ normative values are provided for the whole cohort and separated by
+ sex. For convenient introspection of the morphometric measures,
+ interactive figures were created using the
+ Plotly
+ Python library v5.9.0. The figures allow
+ interactive visualization of normative values for any slice in the
+ PAM50 space and filtering for sex, age decades, and MRI vendors. The
+ figures show values per slice (instead of per vertebral level), to
+ prevent the loss of information that would arise if values were
+ averaged within each vertebral level.
+
+
+ 2.5 Statistical analysis
+ Statistical analysis was conducted using the SciPy Python library
+ v1.10.1
+ (Virtanen
+ et al., 2020). Descriptive statistics, including mean and
+ standard deviation, were computed for age, height, and weight. The
+ Shapiro-Wilk normality test was used to assess data normality.
+ Differences between males and females in age, height, and weight
+ were examined using the Wilcoxon rank-sum test. Morphometric
+ measures in PAM50 space were averaged across participants for each
+ slice and compared between sex and MRI vendors using the Wilcoxon
+ rank-sum test. The significance level was set to
+ alpha = 0.001.
+ The inter-subject coefficient of variation (COV), defined as the
+ ratio of standard deviation and mean, was computed per slice for all
+ morphometrics measures. The COV was then averaged for individual
+ vertebral levels. Additionally, the mean COV for the whole cervical
+ spinal cord was computed as average across all slices.
+
+
+
+ 4. DISCUSSION
+ This study introduced a framework to automatically normalize spinal
+ cord morphometric measures and computed normative metrics from a
+ public database of healthy adults. Normative values were reported in
+ the PAM50 template reference space, which facilitates the comparison
+ of results across past and future studies. Metrics were presented as
+ interactive figures, allowing readers to conveniently explore
+ morphometric values and filter them according to sex, MRI vendor, and
+ age.
+
+ 4.1 Participants
+ Assessment of the spine-generic MRI scans by two
+ experienced radiologists revealed mild spinal cord compression in
+ 24% of volunteers. This finding aligns with previous studies that
+ have reported the prevalence of asymptomatic spinal cord compression
+ in up to 40% of the otherwise healthy population
+ (Kovalova
+ et al., 2016;
+ Smith
+ et al., 2021). Given our objective of constructing a database
+ containing healthy normative morphometric values to minimize
+ variability in future research, we excluded the subjects with mild
+ compression to mitigate potential bias.
+
+
+ 4.2 Normalization to PAM50 anatomical dimensions
+ The proposed normalization approach is performed per slice
+ (instead of per vertebral level), providing a more exhaustive
+ picture of cord morphometry along the superior-inferior axis.
+ Moreover, precise quantification of cord morphometry along the
+ superior-inferior axis could be relevant. For example, in the case
+ of compression spanning only a few mm of cord tissue, it would be
+ desirable to know the cord morphometry on the healthy population at
+ the equivalent spinal cord location (ie: not the entire vertebrae,
+ but a smaller section).
+ Compared to classical image-based registration, our normalization
+ approach does not introduce geometrical image distortions, which may
+ result in inaccuracies when computing morphometric measures.
+ Traditional registration to the PAM50 template involves spinal cord
+ straightening, vertebral alignment between the image and the
+ template, and iterative slice-wise non-linear registration
+ (De
+ Leener et al., 2017). Each of these steps might change the
+ spinal cord shape and contour (see a relevant
+ issue
+ on GitHub).
+
+
+ 4.3 Interactive figures and Normative database
+ Unlike previous studies
+ (De
+ Leener et al., 2018;
+ Horáková
+ et al., 2022;
+ Kato
+ et al., 2012;
+ Taso
+ et al., 2016) that presented normative values using “static”
+ tables, our paper features interactive figures for a more exhaustive
+ and convenient exploration of morphometric results.
+ Furthermore, previous studies, such as those conducted by
+ (De
+ Leener et al., 2018;
+ Kato
+ et al., 2012;
+ Taso
+ et al., 2016), only provided normative values for CSA and AP
+ diameter. Only one recent study
+ (Horáková
+ et al., 2022) also featured transverse diameter, compression
+ ratio, solidity, and torsion. Results presented in this study
+ feature morphometric values for all six metrics simultaneously,
+ offering a convenient way to explore the relationship between
+ individual morphometric measures. This could be particularly useful
+ for assessing changes in spinal anatomy between levels or for
+ identifying levels of spinal cord compression. Additionally,
+ researchers have the option to show or hide traces by clicking on
+ their corresponding legend items, allowing for easy exploration of
+ trends related to sex, age decades, and MRI vendors.
+ The proposed open-source database of normative values in the
+ PAM50 space allows researchers to filter subjects based on
+ demographic and biological factors. Researchers can thus match sex,
+ age, and MRI vendor with their study population and use our database
+ for normalization of their cohort relative to the healthy population
+ with respect to these factors. This is a relevant feature since
+ normalization per sex is the most commonly used normalization factor
+ for spinal cord morphometric measures
+ (Bédard
+ & Cohen-Adad, 2022;
+ Kesenheimer
+ et al., 2021;
+ Mina
+ et al., 2021;
+ Papinutto
+ et al., 2020;
+ Rashid
+ et al., 2006;
+ Rocca
+ et al., 2019).
+
+
+ 4.4 Morphometric measures
+ The CSA values obtained in this study for individual
+ intervertebral discs are in line with CSA measured in 50 healthy
+ subjects
+ (De
+ Leener et al., 2018). For instance, we measured a CSA of
+ 76.61 ± 8.35 mm2 (mean ± standard deviation) for the C3-C4
+ intervertebral disc, while De Leener et al.
+ (2018)
+ obtained 77.46 ± 8.45 mm2 for the same intervertebral disc. In
+ contrast, other studies reported either smaller or larger CSA for
+ the same C3-C4 intervertebral disc. For example, Horáková et al.
+ (2022)
+ obtained a CSA of 71.7 ± 8.2 mm2, while Kesenheimer et al.
+ (2021)
+ measured a CSA of 87.4 ± 8.31 mm2. This can be attributed to various
+ factors, including differences in MRI contrast, variations in
+ population ages, and variations in the segmentation methods
+ used.
+ Another study based on the UK Biobank database (N = 804) measured
+ a CSA of 66.4 ± 6.61 mm2 at C2-C3 vertebral levels
+ (Bédard
+ & Cohen-Adad, 2022). Here, we measured larger CSAs: 73.59
+ ± 7.41 mm2 at C2 and 74.32 ± 7.91 mm2 at C3 vertebral levels. This
+ discrepancy is likely caused by our study relying on T2-w images,
+ whilst Bédard and Cohen-Adad used T1-w images
+ (Bédard
+ & Cohen-Adad, 2022). It has been shown that T2-w scans
+ generally yield larger CSA compared to T1-w scans
+ (Cohen-Adad
+ et al., 2021b).
+ As for AP diameter and transverse diameter, values measured in
+ this study correspond with population estimates from Frostell et al.
+ (2016).
+ For instance, we measured an AP diameter of 7.86 ± 0.56 mm and a
+ transverse diameter of 11.9 ± 0.76 mm for the C2 vertebral level,
+ while Frostell et al.
+ (2016)
+ reported an AP diameter of 7.9 ± 1.6 mm and a transverse diameter of
+ 12.3 ± 2.4 mm for the same vertebral level.
+ Confirming previous studies
+ (Bédard
+ & Cohen-Adad, 2022;
+ Engl
+ et al., 2013;
+ Papinutto
+ et al., 2020;
+ Rashid
+ et al., 2006;
+ Solstrand
+ Dahlberg et al., 2020;
+ Yanase
+ et al., 2006), we showed that females have smaller CSA
+ relative to males across all vertebral levels. Smaller spinal cord
+ size is also mirrored by lower AP and transverse diameters in
+ females compared to males.
+ The increase in CSA around vertebral levels C4-C5 indicates the
+ location of cervical enlargement, the source of the large spinal
+ nerves that supply the upper limbs. This finding is consistent with
+ anatomical textbooks
+ (Standring,
+ 2020) and previous studies
+ (De
+ Leener et al., 2018;
+ Frostell
+ et al., 2016;
+ Horáková
+ et al., 2022;
+ Martin
+ et al., 2017b;
+ Mina
+ et al., 2021;
+ Rocca
+ et al., 2019). After the cervical enlargement (i.e., below
+ level C5), the spinal cord becomes smaller, which is mirrored by the
+ decrease in CSA, AP diameter, and transverse diameter. The decrease
+ in AP diameter along the superior-inferior direction, along with the
+ changing trends in compression ratio and eccentricity, corresponds
+ to the fact that the spinal cord is not cylindrical but rather
+ changes its shape across levels from circular shape at C1 and C2
+ levels to a more elliptical shape around levels C5 and C6
+ (Standring,
+ 2020).
+ Because various morphometric measures exhibited differing levels
+ of inter-subject variability (indicated by COV), we hypothesize that
+ normalizing measures with greater inter-subject variability, such as
+ CSA, would yield a more pronounced impact compared to normalizing
+ measures with lower inter-subject variability, such as solidity. We
+ measured a COV of 10.1% and 10.8% for the CSA at the C2 and C3
+ vertebral levels, respectively. These values are similar to the
+ 9.96% COV reported by Bédard and Cohen-Adad for T1-w images at the
+ C2-C3 level
+ (Bédard
+ & Cohen-Adad, 2022).
+ The variability of morphometric measures between MRI vendors
+ might be explained by differences in sequence parameters and/or
+ reconstruction filters between vendors
+ (Cohen-Adad
+ et al., 2021b).
+
+
+ 4.5 Limitations and Future Work
+ The T2-w images from the open-access
+ spine-generic dataset cover only the cervical
+ spinal cord and have a relatively narrow age range (with 93.6% of
+ subjects aged 21 to 40 years). Despite this limitation, it remains
+ the largest open-source database of multi-contrast spinal cord MRI
+ data. We welcome future contributions of additional subjects across
+ different age groups and with data that encompasses the entire
+ spinal cord.
+ We are aware that the morphometric measures were derived solely
+ from T2-w MRI contrast and using the segmentation method trained
+ specifically for this contrast
+ (Gros
+ et al., 2019). This has to be considered when comparing with
+ other MRI contrasts, such as T1-w, which showed a smaller CSA
+ compared to T2-w
+ (Cohen-Adad
+ et al., 2021b). This might be mitigated in the future using a
+ contrast-agnostic segmentation algorithm
+ (Bédard,
+ 2023).
+ Future efforts will focus on validating the proposed methods in
+ pathologies such as traumatic and non-traumatic spinal cord injury
+ and multiple sclerosis. This validation process will provide
+ valuable insights into the applicability and accuracy of the methods
+ in the context of various spinal cord conditions.
+
+
+ 4.6 Conclusions
+ We introduced a new approach for the normalization of spinal cord
+ morphometric measures using the PAM50 spinal cord template. We built
+ an interactive database of spinal cord morphometric values across
+ 203 healthy adults. The database can be used to normalize spinal
+ cord morphometric features, stratified according to factors such as
+ sex, age, and MRI vendors. This database can also be used to further
+ inspect demographic, biological and image acquisition factors
+ associated with inter-subject variability.
+ The proposed methodology and results are open-source and fully
+ reproducible. The database and normalization method is applicable to
+ new datasets via the Spinal Cord Toolbox (SCT)
+ v6.0 and higher.
+
+
+
+