Tags: DCAN-Labs/nibabies
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24.0.0 - alpha 1 This preliminary release alters some outputs when using MCRIBS surface reconstruction, as well as updates reports. * MAINT: Update to latest migas API (nipreps#326) * FIX: T2star map MNI scaling (nipreps#320) * ENH: Alter outputs when MCRIBS reconstruction is used (nipreps#329) * ENH: Use nireports for Report generation + add reportlet per reconstruction (nipreps#328)
23.1.0 (November 22, 2023) The next minor release of *NiBabies*, this release includes a number of new goodies, including: M-CRIB-S (Adamson et al., https://www.nature.com/articles/s41598-020-61326-2), has shown to improve performance in participants under 9 months. If you would like to try this method, add the following to your command: `--surface-recon-method mcribs`. Note: Currently, a T2w image and pre-computed segmentation derivative must be provided to run mcribs. *NiBabies* now automatically parses the BIDS directory for participant ages, first searching in the participant's `session.tsv`, and falling back to `participants.tsv`. This simplifies batch submissions including multiple subjects & sessions. As a result, the `--age-months` flag has been deprecated, and will be removed in a later release. An option to determine and exclude high-variance voxels from being projected to the surface when creating CIFTI files. To enable this, add `--project-goodvoxels` to your command. Running *NiBabies* is now less restrictive, and will still process data missing either a T1w / T2w image. However, for best results, it is recommended to collect and include both for processing. Previous, *NiBabies* expected input from the `--derivatives` flag to be in T1w space, using the entity `space-orig`. This has now been changed to support derivatives in either T1w or T2w space. For more information, please see https://nibabies.readthedocs.io/en/23.1.0/faqs.html#leveraging-precomputed-results * CI: Purge codecov python package (nipreps#282) * DKR: Upgrade Docker base, c3d (nipreps#275) * DKR: Add M-CRIB-S to Docker container (nipreps#283) * DKR: Update dependencies, split into multi-stage build * ENH: Add option to exclude projecting high variance voxels to surface (nipreps#278) * ENH: Resample morphometrics to fsLR CIFTI-2 files when outputing CIFTIs (nipreps#279) * ENH: Add MCRIBReconAll as alternative surface reconstruction method (nipreps#283) * ENH: Reorder anatomical processsing, run ANTs DenoiseImage on anatomicals (nipreps#286) * ENH: Extract participant ages from BIDS sources, deprecate `--age-months` (nipreps#287) * ENH: Dilate BOLD mask by 2 voxels to prevent over-aggressive masking degrading T2star map estimation (nipreps#296) * ENH: Allow precomputed derivatives in T1w or T2w space (nipreps#305) * ENH: Add separate workflow for single anatomical processing (nipreps#316) * FIX: Improve free memory estimation (nipreps#284) * FIX: Ensure age is extracted from sessions file (nipreps#291) * FIX: Restore CIFTI medial wall masking, subcortical volume LAS reorientation (nipreps#298) * FIX: Recify "goodvoxels" surface projection (nipreps#301) * FIX: Connect derivatives mask to mcribs recon (nipreps#323) * MAINT: Drop TemplateFlowSelect patches (nipreps#290)
23.1.0rc1 (November 10, 2023) The next minor release of *NiBabies*, this release includes a number of new goodies, including: M-CRIB-S (Adamson et al., https://www.nature.com/articles/s41598-020-61326-2), has shown to improve performance in participants under 9 months. If you would like to try this method, add the following to your command: `--surface-recon-method mcribs`. Note: Currently, a T2w image and pre-computed segmentation derivative must be provided to run mcribs. *NiBabies* now automatically parses the BIDS directory for participant ages, first searching in the participant's `session.tsv`, and falling back to `participants.tsv`. This simplifies batch submissions including multiple subjects & sessions. As a result, the `--age-months` flag has been deprecated, and will be removed in a later release. An option to determine and exclude high-variance voxels from being projected to the surface when creating CIFTI files. To enable this, add `--project-goodvoxels` to your command. Running *NiBabies* is now less restrictive, and will still process data missing either a T1w / T2w image. However, for best results, it is recommended to collect and include both for processing. * CI: Purge codecov python package (nipreps#282) * DKR: Upgrade Docker base, c3d (nipreps#275) * DKR: Add M-CRIB-S to Docker container (nipreps#283) * DKR: Update dependencies, split into multi-stage build * ENH: Add option to exclude projecting high variance voxels to surface (nipreps#278) * ENH: Resample morphometrics to fsLR CIFTI-2 files when outputing CIFTIs (nipreps#279) * ENH: Add MCRIBReconAll as alternative surface reconstruction method (nipreps#283) * ENH: Reorder anatomical processsing, run ANTs DenoiseImage on anatomicals (nipreps#286) * ENH: Extract participant ages from BIDS sources, deprecate `--age-months` (nipreps#287) * ENH: Dilate BOLD mask by 2 voxels to prevent over-aggressive masking degrading T2star map estimation (nipreps#296) * ENH: Allow precomputed derivatives in T1w or T2w space (nipreps#305) * ENH: Add separate workflow for single anatomical processing (nipreps#316) * FIX: Improve free memory estimation (nipreps#284) * FIX: Ensure age is extracted from sessions file (nipreps#291) * FIX: Restore CIFTI medial wall masking, subcortical volume LAS reorientation (nipreps#298) * FIX: Recify "goodvoxels" surface projection (nipreps#301) * MAINT: Drop TemplateFlowSelect patches (nipreps#290)
23.1.0rc0 (July 13, 2023) A release candidate of the next minor release of *NiBabies*, this release includes a number of new goodies, including: M-CRIB-S (Adamson et al., https://www.nature.com/articles/s41598-020-61326-2), has shown to improve performance in participants under 9 months. If you would like to try this method, add the following to your command: `--surface-recon-method mcribs`. Note: Currently, a pre-computed segmentation derivative must be provided to run mcribs. *NiBabies* now automatically parses the BIDS directory for participant ages, first searching in the participant's `session.tsv`, and falling back to `participants.tsv`. This simplifies batch submissions including multiple subjects & sessions. As a result, the `--age-months` flag has been deprecated, and will be removed in a later release. An option to determine and exclude high-variance voxels from being projected to the surface when creating CIFTI files. To enable this, add `--project-goodvoxels` to your command. * CI: Purge codecov python package (nipreps#282) * DKR: Upgrade Docker base, c3d (nipreps#275) * DKR: Add M-CRIB-S to Docker container (nipreps#283) * DKR: Update dependencies, split into multi-stage build * ENH: Add option to exclude projecting high variance voxels to surface (nipreps#278) * ENH: Resample morphometrics to fsLR CIFTI-2 files when outputing CIFTIs (nipreps#279) * ENH: Add MCRIBReconAll as alternative surface reconstruction method (nipreps#283) * ENH: Reorder anatomical processsing, run ANTs DenoiseImage on anatomicals (nipreps#286) * ENH: Extract participant ages from BIDS sources, deprecate `--age-months` (nipreps#287) * ENH: Dilate BOLD mask by 2 voxels to prevent over-aggressive masking degrading T2star map estimation (nipreps#296) * FIX: Improve free memory estimation (nipreps#284) * FIX: Ensure age is extracted from sessions file (nipreps#291) * FIX: Restore CIFTI medial wall masking, subcortical volume LAS reorientation (nipreps#298) * FIX: Recify "goodvoxels" surface projection (nipreps#301) * MAINT: Drop TemplateFlowSelect patches (nipreps#290)
23.0.0 (January 23, 2023) New year, new *NiBabies* minor series! Some of the highlights of this release include: - New run-wise BOLD reference generation, prioritizing single-band references if available, unless avoided with the `--ignore sbrefs` flag. - New output: Preprocessed T2w in T1w space. A full list of changes can be found below. * ENH: Runwise bold reference generation (nipreps#268) * ENH: Add preprocessed T2w volume to outputs (nipreps#271) * MAINT: Drop versioneer for hatch backend, fully embrace pyproject.toml (nipreps#265) * MAINT: Rotate CircleCI secrets and setup up org-level context (nipreps#266) * CI: Bump convenience images, limit datalad (nipreps#267) * FIX: Remove legacy CIFTI variant support (nipreps#264)
22.2.0 (December 13, 2022) The final *NiBabies* minor series of 2022! Some highlights of the new additions in this release series includes: - surface morphometrics outputs, including cortical thickness - T2star maps for multiecho data, projected to target output spaces This series will be the last to support Python 3.7. A full list of changes can be found below. * FIX: Remove cortex masking during vol2surf sampling (nipreps#258) * ENH: Improve migas telemetry (nipreps#257) * CI: GitHub actions update (nipreps#256) * ENH: Add morphometric outputs (nipreps#255) * ENH: Output T2star maps for multiecho data (nipreps#252) * FIX: Use the binarized output from the brain extraction (nipreps#246) * DOC: Add long description including background/significance (nipreps#243) * CI: Fix docker credential error (nipreps#244) * DOC: Advertise nipreps community pages, add section on contributions (nipreps#242)
22.1.3 (September 12, 2022) This patch release includes a vital fix for susceptibility distortion correction on multi-echo data. * FIX: Field name for multi-echo fieldmap correction (nipreps#233)
22.1.1 (August 15, 2022) ======================== A bugfix release that includes missing files needed to run `infant_recon_all`. * FIX: Add missing shared object for `infant_recon_all` (nipreps#231) * RF: `migas` reporting (nipreps#230)
22.1.0 (August 2, 2022) ======================= A new minor release! The 22.1.x series of *NiBabies* includes: - Improved alignment between FreeSurfer outputs and processed anatomical. - Decreased memory usage while running across multiple processes (default). - Fix to multi-echo processing in cases where an optimally combined file of all echoes was missing. - Fix to the subcortical CIFTI to be in *LAS* orientation. * FIX: Correct fsnative <-> anatomical transforms (nipreps#223) * FIX: Vastly improve multi-echo handling (nipreps#220) * ENH: Add migas telemetry to nibabies (nipreps#226) * ENH: Add interface for reorienting images (nipreps#229) * DOCKER: Bump Python to 3.9 (nipreps#221) * RF/ENH: Rework workflow generation (nipreps#219)
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