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Abstract #4458

Evaluation of Denoising Deep Convolutional Neural Network for Double Diffusion Encoding Technique

Hiroshi Kusahara1, Masanori Ozaki2, Masahiro Abe1, Koji Kamagata3, Masaaki Hori4, and Shigeki Aoki3
1Advanced MRI development PJ Team, Canon Medical Systems Corporation, Kanagawa, Japan, 2Research&Development Center, Canon Medical Systems Corporation, Kanagawa, Japan, 3Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan, 4Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan

Double Diffusion Encoding (DDE) is a diffusion measurement technique that applies two directions of diffusion encoding in parallel and orthogonal directions and can calculate μFA that can evaluate detailed information of anisotropy in voxels. However, since the diffusion encoding method generally acquired many directions, the acquisition time becomes long. In this study, we evaluated DDE technique applying denoising DLR recently developing. It was demonstrated that the dDLR techniques are capable of generating DDE with higher SNR compared to normal DDE, with the additional benefit of being able to optimize the acquisition time and number of acquisitions without affecting μFA.

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