QSIPrep: A robust and unified workflow for preprocessing and reconstructing diffusion MRI

Matthew Cieslak, PhD Presenter
University of Pennsylvania
United States
Software Demonstrations 
Although diffusion-weighted magnetic resonance imaging (dMRI) can take many forms, they all sample q-space in order to characterize water diffusion. Numerous pipelines and software platforms have been built for processing dMRI data, but most work on only a subset of sampling schemes, or implement only parts of the processing workflow. Comparisons across methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions, among others. Here we introduce QSIPrep, a new processing pipeline for diffusion images that is compatible with virtually all dMRI sampling schemes via a uniform, containerized application. Preprocessing includes denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Individual algorithms from a diverse set of cutting-edge software suites are combined to capitalize upon their complementary strengths. Throughout, QSIPrep provides both visual and quantitative measures of data quality and "glass-box" methods reporting. Together, these features allow for easy implementation of best practices while simultaneously maximizing reproducibility.