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Abstract #4329

Acceleration of multidimensional diffusion MRI data acquisition and post-processing using convolutional neural networks

Yuan Zheng1, Tao Feng1, Sirui Li2, Wenbo Sun2, Qing Wei3, Samo Lasic4, Danielle van Westen5, Karin Karin Bryskhe4, Daniel Topgaard4,5, and Haibo Xu2
1UIH America, Houston, TX, United States, 2Zhongnan Hospital of Wuhan University, Wuhan, China, 3United Imaging Healthcare, Shanghai, China, 4Random Walk Imaging, Lund, Sweden, 5Lund University, Lund, Sweden

Multidimensional diffusion MRI (dMRI) is a powerful tool that even in its simplest form provides more detailed microstructural information than conventional dMRI, such as microscopic anisotropy (µFA) unconfounded by orientation dispersion. However, it requires multiple diffusion encoding modes (usually directional and isotropic encodings) and, for the more advanced versions, prolonged scan and post-processing times. We proposed using convolutional neural networks (CNN) to accelerate multidimensional dMRI data acquisition and analysis, and have demonstrated that satisfactory µFA maps can be generated in real-time with only 50% of the encodings, which might help to better adapt multidimensional dMRI to clinical practices.

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