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README.rst

QSIprep: Preprocessing and analysis of q-space images

Documentation Status

About

qsiprep configures pipelines for processing diffusion-weighted MRI (dMRI) data. The two main features of this software are

  1. A novel :ref:`preprocessing_def` pipeline for non-DTI q-space imaging sequences
  2. A system for building a :ref:`reconstruction_def` pipeline that includes algorithms from Dipy_, MRTrix_, `DSI Studio`_ and others.

Preprocessing

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.

Reconstruction

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:

Post-eddy

You have already preprocessed your diffusion data using eddy, and want to reconstruct it using MAPMRI and save the ODFs in a DSI Studio fib.gz file 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 3dSHORE and 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.

Note

The 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 FRMIPREP and qsiprep.

  1. 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.
  2. 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.
  3. 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.
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