LayNii v2.7.0

Release before OHBM 2024. This release is focused on providing new programs that are focused on computing spatial gradients on magnitude and phase images. These new programs are:

• LN2_GRADIENTS
• LN2_LAPLACIAN
• LN2_GRAMAG
• LN2_PHASE_GRADIENTS
• LN2_PHASE_LAPLACIAN
• LN2_PHASE_JOLT

LayNii v2.6.0

Release after the OHBM abstract deadline. Mainly for start making the new and experimental phase processing tools available to other interested researchers.

New !!! experimental !!! programs

• LN2_PHASE_JOLT: Compute L1 norm of phase image second spatial derivatives.

LayNii v2.5.3

Version 2.5.0 is a relatively minor release before OHBM 2023, introducing the new and major LN2_RIM_POLISH program. As this versions is released shortly after v2.4.0, please also refer to v2.4.0 release notes for other news if you are switching from earlier versions.

New programs

• LN2_RIM_POLISH : Smooth the cortical gray matter borders. Designed to be especially used after manual corrections. Default parameters are optimized for 0.2 mm isotropic images. See this video for an example usage: https://youtu.be/Do77pdTwSy8?t=1124 at around 19:00 . Also see the related issue at: #77

Minor fixes

• [2.5.1] Change programs printing LayNii v2.4.0 to LayNii v2.5.1 upon execution.
• [2.5.2] Add masking options in LN_SKEW and fix several typos in the docstrings.
• [2.5.3] Add safety check for scl slope = 0 in nifti header (see #88 for further details).

LayNii v2.5.2

Version 2.5.0 is a relatively minor release before OHBM 2023, introducing the new and major LN2_RIM_POLISH program. As this versions is released shortly after v2.4.0, please also refer to v2.4.0 release notes for other news if you are switching from earlier versions.

New programs

• LN2_RIM_POLISH : Smooth the cortical gray matter borders. Designed to be especially used after manual corrections. Default parameters are optimized for 0.2 mm isotropic images. See this video for an example usage: https://youtu.be/Do77pdTwSy8?t=1124 at around 19:00 . Also see the related issue at: #77

Minor fixes

• [2.5.1] Change programs printing LayNii v2.4.0 to LayNii v2.5.1 upon execution.
• [2.5.2] Add masking options in LN_SKEW and fix several typos in the docstrings.

LayNii v2.5.1

Version 2.5.0 is a relatively minor release before OHBM 2023, introducing the new and major LN2_RIM_POLISH program. As this versions is released shortly after v2.4.0, please also refer to v2.4.0 release notes for other news if you are switching from earlier versions.

New programs

• LN2_RIM_POLISH : Smooth the cortical gray matter borders. Designed to be especially used after manual corrections. Default parameters are optimized for 0.2 mm isotropic images. See this video for an example usage: https://youtu.be/Do77pdTwSy8?t=1124 at around 19:00 . Also see the related issue at: #77

Minor fixes

• Change programs printing LayNii v2.4.0 to LayNii v2.5.1 upon execution.

LayNii v2.4.0

Modifications

• LN2_MULTILATERATE: Speed up perimeter update. #75
• LN2_VORONOI: Add maximum distance parameter (useful for very large files e.g. 0.1 mm whole brain). #75
• LN2_COLUMNS and LN2_IFPOINTS: Now prints the maximum distance between centroids/points. Useful for understanding the approximately average distance between centroids/points. #70

New programs

• LN2_PATCH_FLATTEN_2D : For flattening 2D slices (e.g. histology data). See this video for usage: https://youtu.be/WUgaQBJkRPA14 . #71
• LN2_PATCH_UNFLATTEN: Unflattening for 3D flattened files (e.g. vitual Petri dishes, cakes ...). See the last 15 minutes of this video for and example usage: https://youtu.be/tIuKG3rtVk4 . #69

Experimental programs and changes

There are the new programs which might be modified without concerning backwards compatibility. They are highlighted in the makefile and will be removed from that section once stabilized.

• LN2_GRAMAG: Compute gradient magnitude images. Can compute phase image gradient magnitudes correctly when using circular flag. #65
• LN2_NEIGHBORS: Find 1st order neighbors of an any given input containing integers (segmentations, parcellations, custom regions of interests etc.). This program yields a comma spearated values file (.CSV) as the default output. In the future we might make use of such neighborhood information files in LayNii. #66
• LN2_UVD_FILTER: Add maximum value based depth peak detection -peak_d. We might work on the outputs and change the terminology in the future. #76

LayNii v2.3.0

New programs

New LN2_HEXBIN program is added for generating hexagonal tiling on UVD coordinates.

Modifications

• Docker improvements contributed by @Remi-Gau .
• Citation.cff improvements.
• LN2_PATCH_FLATTEN 4D nifti input and output capability is now added.
• LN2_MULTILATERATE and LN2_PATCH_FLATTEN references are updated to:
  • Gulban, O. F., Bollmann s., Huber L., Wagstyl K., Goebel R., Poser B. A., Kay K., Ivanov D. “Mesoscopic in Vivo Human T2* Dataset Acquired Using Quantitative MRI at 7 Tesla.” NeuroImage 264 (December 2022): 119733. https://doi.org/10.1016/j.neuroimage.2022.119733.

LayNii v2.2.1

Purpose of the release

LN2_MULTILATERATE and LN2_PATCH_FLATTEN focused release to serve as a checkpoint for:

• Gulban, O. F., Bollmann, S., Huber, R., Wagstyl, K., Goebel, R., Poser, B. A., Kay, K., Ivanov, D. (2021). Mesoscopic Quantification of Cortical Architecture in the Living Human Brain. BioRxiv. https://doi.org/10.1101/2021.11.25.470023

Modifications

• LN2_LAYERS now outputs approximately equal number of voxels for each layer with -equal_counts flag. This option can be useful for low resolution inputs (e.g. > 0.5 mm iso.). Note: when activating this flag, it will equalize the number of voxels for each layer without respecting equi-distant and equi-volume cortical depth measurements.
• LN_RAGRUG now has -scale parameter to create rectangular tessellations at lower resolutions (integer multiples of the input voxel dimensions.
• Binaries for MACOS M1 chip will be included from now on.
• [v2.2.1] Minor fixes to outputs of LN2_MULTILATERATE and help menu improvements.

LayNii v2.2.0

Purpose of the release

LN2_MULTILATERATE and LN2_PATCH_FLATTEN focused release to serve as a checkpoint for:

• Gulban, O. F., Bollmann, S., Huber, R., Wagstyl, K., Goebel, R., Poser, B. A., Kay, K., Ivanov, D. (2021). Mesoscopic Quantification of Cortical Architecture in the Living Human Brain. BioRxiv. https://doi.org/10.1101/2021.11.25.470023

Modifications

• LN2_LAYERS now outputs approximately equal number of voxels for each layer with -equal_counts flag. This option can be useful for low resolution inputs (e.g. > 0.5 mm iso.). Note: when activating this flag, it will equalize the number of voxels for each layer without respecting equi-distant and equi-volume cortical depth measurements.
• LN_RAGRUG now has -scale parameter to create rectangular tessellations at lower resolutions (integer multiples of the input voxel dimensions.
• Binaries for MACOS M1 chip will be included from now on.

LayNii v2.1.1

New programs

LN2_GEODISTANCES to measure geodesic distances in a set of voxels from another set of voxels.
LN2_IFPOINTS is a simplified (and more general) version of the iterative farthest points algorithm used in LN2_COLUMNS.
LN2_MASK is intended to help with voxel section for layer-profile extraction.
LN2_BORDERIZE is introduced to find borders in an integer nifti (e.g. segmentation files, rim files).

Modifications

LN2_MULTILATERATE algorithm is significantly improved through using pin axis geodesic distances rather than point based geodesic distances.
LN2_LAYERS now have a very minimal masked smoothing applied to the equivolume and equidistance metrics. This is done to mitigate the discrete sampling effects present close to the borders.
[2.1.1] LN2_PATCH_FLATTEN minor improvements.