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Abstract #4348

Rapid reconstruction of Blip up-down circular EPI (BUDA-cEPI) for distortion-free dMRI using an Unrolled Network with U-Net as Priors

Uten Yarach1, Itthi Chatnuntawech2, Congyu Liao3, Surat Teerapittayanon2, Siddharth Srinivasan Iyer4,5, Tae Hyung Kim6,7, Jaejin Cho6,7, Berkin Bilgic6,7, Yuxin Hu8, Brian Hargreaves3,8,9, and Kawin Setsompop3,8
1Department of Radiologic Technology, Faculty of Associated Medical Science, Chiang Mai University, Chiang Mai, Thailand, 2National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani, Thailand, 3Department of Radiology, Stanford University, Stanford, CA, United States, 4Department of Radiology, Stanford University, Stanford, Stanford, CA, United States, 5Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 6Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 7Department of Radiology, Harvard Medical School, Boston, MA, United States, 8Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 9Department of Bioengineering, Stanford University, Stanford, CA, United States


Blip-up and Blip-down EPI (BUDA) is a rapid, distortion-free imaging method for diffusion-imaging and quantitative-imaging. Recently, we developed BUDA-circular-EPI (BUDA-cEPI) to shorten the readout-train and reduce T2* blurring for high-resolution applications. In this work, we further improve encoding efficiency of BUDA-cEPI by leveraging partial-Fourier in both phase-encode and readout directions, where complimentary conjugate k-space information from the blip-up and blip-down EPI-shots and S-LORAKS constraint are used to effectively fill-out missing k-space. While effective, S-LORAKS is computationally expensive. To enable clinical deployment, we also proposed a machine-learning reconstruction derived from RUN-UP (unrolled K-I network) that accelerates reconstruction by >300x.

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