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Abstract #4405

DeepHIBRID: How to condense the sampling in the k-q joint space for microstructural diffusion metric estimation empowered by deep learning

Qiuyun Fan1, Qiyuan Tian1, Chanon Ngamsombat1, and Susie Y. Huang1
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States

Conventional diffusion imaging protocols may require tens or hundreds of samples in the q-space to generate reliable maps. Knowing that the k-q joint space is highly redundant and given the tradeoffs between k, q and SNR, we trained a deep convolutional neural network using a HIgh B-value and high Resolution Integrated Diffusion (HIBRID) sampling scheme, dubbed DeepHIBRID. We show DeepHIBRID outperforms conventional sampling schemes, and is capable of outputting 14 synthesized diffusion metric maps simultaneously with only 10 input images, without sacrificing the quality of the output maps, using 30x angular downsampling.

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