QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data

Abstract

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

Extended Data Fig. 1. Diffusion imaging data used in QSIPrep development and evaluation.

Cartesian (DSI), random (CS-DSI), and shelled (single-shell DTI and multi-shell) sequences were used to test the preprocessing and reconstruction workflows in QSIPrep. Sequences varied widely in their maximum b-value (1000–5000 s/mm²), number of q-space samples (64–789) and voxel size (1.5–2.3 mm). The row colors represent these schemes across all figures. The colors in the HCP-Lifespan image indicate that these samples came from different scans, grouped by phase-encoding direction.

Extended Data Fig. 2. Comparing added smoothness from QSIPrep and previous pipelines.

Preprocessing generally increases the spatial smoothness of images relative to the raw images. Here the raw image smoothness (x-axis) is compared to the same images after being processed by the published pipeline for each dataset (left) and QSIPrep (right). The direct comparison between QSIPrep and the Previous Pipeline is presented in Fig. 2.

Extended Data Fig. 3. QSIPrep reconstruction workflows produce comparable output across diverse sampling schemes and reconstruction methods.

Four sampling schemes each reconstructed using four methods: GQI from DSI Studio, multi-tissue CSD from MRtrix, and MAPL from Dipy. ODF fields are shown in two white matter regions (left), a single fiber area in the corpus callosum (top) and a crossing fiber region in the centrum semiovale (bottom). The middle panel shows ODFs reconstructed in the single fiber region, and the right panel shows ODFs reconstructed in the crossing fiber region for the four sampling schemes (rows) and the three reconstruction methods (columns).

Fig 1 |. QSIPrep workflows.

QSIPrep includes preprocessing (left column) and reconstruction (right column) workflows. BIDS data enters the workflow at the top left, following the blue arrow sequentially through the possible steps. The outputs from the preprocessing pipeline are inputs for the reconstruction workflows, which includes reconstruction methods from MRtrix3, DSI Studio, and DIPY. A matrix of orientation distribution functions (ODF)s shows a fiber crossing reconstructed from multiple sampling schemes with multiple methods in QSIPrep. Gray arrows labeled “Convert Formats” indicate that a reconstruction from one software package can be converted to be used in the destination software for further processing (e.g., DIPY reconstructions can be used for tractography in MRtrix3). For further details on options for denoising workflows, see Supplementary Figure 1.

Fig. 2 |. QSIPrep improves image quality without additional smoothing.

a,b, Comparison of image smoothness (FWHM, a) and data quality (NDC, b) produced by QSIPrep and previously published pipelines tailored for each acquisition scheme for shelled schemes. c,d, Comparison of image smoothness (c) and data quality (d) between QSIprep and raw data for nonshelled schemes (for example, Cartesian and random sampling).