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Abstract #4358

Towards individual direction-based deep learning of diffusion weighted images for standard diffusion model analysis.

Peidong He1, Zifei Liang1, Marco Muccio1, Florian Knoll1, Jiangyang Zhang1, and Yulin Ge1
1Department of Radiology, New York University School of Medicine, New York, NY, United States

In diffusion MRI, a higher number of gradient directions benefit to SNR and robustness of fiber rotational invariant estimation for tensor computation, however, it makes the acquisition time to lengthy to be clinically viable. This study was to apply a deep learning approach to generate new individual direction diffusion weighted (DW) source images (e.g., 60 more directions) from original 30 direction DW images based on high-angular-resolution (90 directions) dataset. Such an approach not only significantly reduce the scan time using one-third original DW images, but also be able to compute dMRI-derived parametric maps using a standard tensor model.

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