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 Iyer^4,5, Tae Hyung Kim^6,7, Jaejin Cho^6,7, Berkin Bilgic^6,7, Yuxin Hu⁸, Brian Hargreaves^3,8,9, and Kawin Setsompop^3,8

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

Synopsis

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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