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• W4321606526 abstract "Abstract Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q -space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q -space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that 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. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications." @default.
• W4321606526 created "2023-02-24" @default.
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• W4321606526 date "2023-02-23" @default.
• W4321606526 modified "2023-10-03" @default.
• W4321606526 title "Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging" @default.
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• W4321606526 doi "https://doi.org/10.1101/2023.02.22.529546" @default.
• W4321606526 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36865219" @default.
• W4321606526 hasPublicationYear "2023" @default.
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