[PDF] QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI | Semantic Scholar (2024)

QSIPrep is a software platform for processing of most diffusion MRI datasets and ensures that adequate workflows are used, and drawing on a diverse set of software suites to capitalize on their complementary strengths facilitates the implementation of best practices forprocessing of diffusion images.

The iDIO pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one‐click solution for dMRI data analysis, via adaptive configuration for a set of suggested processing steps based on the image header of the input data.

The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA and was shown to be effective on externally available datasets.

CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme, underscoring its promise for both clinical and research applications.

The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to demonstrate the reliability of computational analysis pipelines in neuroimaging.

It is shown that multilayer networks represent a biologically and clinically meaningful framework to model multimodal MRI data, with disruption of the core-periphery structure emerging as a potential novel biomarker for disease severity and cognitive impairment in multiple sclerosis.

The spatial extent and temporal trajectory of free-water changes in patients with subcortical stroke and their relationship to symptoms, as well as lesion evolution are explored, indicating a dynamic parenchymal response to the initial insult characterized by vasogenic edema, cellular damage and white matter atrophy.

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Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface, and has implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography.

The analysis result showed that the diffusion signals, anisotropy, diffusivity, and connectivity matrix of the HARDI converted from multi-shell and DSI were highly correlated with those of theHARDI acquired on the MR scanner, with correlation coefficients around 0.8~0.9.

The Brain Imaging Data Structure (BIDS) is developed, a standard for organizing and describing MRI datasets that uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

A clinically feasible imaging scheme that uses built-in DTI scanning sequences to obtain high angular resolution images that are reconstructed by GQI method, which is similar to the grid sampling scheme used in DSI, but the arrangement combines two spheres instead.

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