Design a Arcana analysis class that implements structural MRI preprocessing and analysis to be used stand-alone or as inputs to other pipelines (i.e dMRI, fMRI, etc.)
Benefit Hypothesis
If structural MRI preprocessing is implemented, anatomical biomarkers such as total brain volume or ROIs volumes can be derived
If this preprocessing is implemented in Arcana analysis class, then outputs can be used as part of input data to wider analyses involving diffusion or functional MRI, and parts of the workflow can be overwritten for a customised application
Acceptance Criteria
[ ] sMRI BIDS App can be run in XNAT container service
[ ] Standard Freesurfer preprocessing (T1/T2) implemented in the Arcana analysis class: brain extraction and segmentation, surface-based brain parcellation/reconstruction and analysis - including full output folder of recon-all
[ ] Boundary Element Model implemented in the Arcana analysis class: Input: T1; Output: inner skull, outer skull, scalp skin surfaces
[ ] Group template made in the Arcana analysis class: template construction, comparison of the uploaded structural image with a structural template, optional WM segmentation or template comparisons for FLAIR.
[ ] Registration implemented in the Arcana analysis class: non-linear registration to a smooth group-average template, robust registration algorithms for rigid alignment of scans to remove small movement artefacts in multi-modality imaging
[ ] Test all analysis pipelines in a testing framework
[ ] DeepBET brain extraction tool implementation in pydra
Description
Design a Arcana analysis class that implements structural MRI preprocessing and analysis to be used stand-alone or as inputs to other pipelines (i.e dMRI, fMRI, etc.)
Benefit Hypothesis
Acceptance Criteria