QSIprep: Preprocessing and analysis of q-space images
qsiprep configures pipelines for processing diffusion-weighted MRI (dMRI) data.
The two main features of this software are
The preprocessing pipelines are designed to perform preprocessing and reconstruction of non-DTI q-space images. Non-DTI means that the imaging sequence used either:
- A multi-shell HARDI sampling scheme
- A Cartesian grid (aka DSI) sampling scheme
- A random q-space sampling scheme (eg for compressed sensing)
QSIprep's head motion correction algorithm relies on voxelwise estimates of ensemble average propagators (EAPs), which require angular and radial variability in the sampling scheme. The preprocessing workflow performs head motion correction, susceptibility distortion correction, MP-PCA denoising, coregistration to T1w images, spatial normalization using ANTs_ and tissue segmentation.
For DTI and multi-shell data, the preprocessing tools available in FSL, MRTrix_
and others are very good. The outputs from these preprocessing pipelines can
be named to mimic the :ref:`outputs` from the
qsiprep preprocessing pipeline
and then reconstructed using workflows from our curated set of :ref:`recon_workflows`.
Example use cases
Consider the following use-cases:
You have already preprocessed your diffusion data using
eddy, and want to reconstruct it using
MAPMRIand save the ODFs in a DSI Studio
fib.gzfile to perform fiber tracking and connectivity analysis.
CS-DSI group analysis
You've acquired a compressed sensing DSI scan and would like to perform motion correction, coregistration to your T1w image and spatial normalization to MNI space. Then reconstruct ensemble average propagators using regularized
3dSHOREand estimate the return-to-origin probability in MNI Space.
Fixel-based analysis on DSI data
You have a classic Cartesian grid DSI dataset that you want to motion-correct and estimate ODFs with in T1 space. Then you want to convert these ODFs to the MRTrix sh representation and perform fixel-based analysis.
These are examples of pipelines that can be created and run through qsiprep. These pipelines use Nipype and are therefore able to run on multiple cores. All the dependencies of this software are included in the Docker_ image.
qsiprep pipeline uses much of the code from
FMRIPREP. It is critical
to note that the similarities in the code do not imply that the authors of
FMRIPREP in any way endorse or support this code or its pipelines. There are
also some noteworthy differences between
- All images are conformed to LPS+ instead of RAS+. This greatly simplified spatial operations on vectors / vector images within ANTs and working with DSI Studio's internal data model.
- Co-registration is performed with ANTs instead of FSL or FreeSurfer. We have removed FSL and FreeSurfer as much as we could from the pipeline in an effort to keep the licensing of bundled software as permissive as possible. Our testing also showed that ANTs was more robust for co-registering b0 images to T1w images.
- It is common to scan multiple separate DWI sequences that are ultimately supposed to be combined for analysis. Whether scans are combined or not will alter when denoising is applied.