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Abstract #1250

Robust high-quality multi-shot EPI with low-rank prior and machine learning

Berkin Bilgic1, Congyu Liao1, Mary Kate Manhard1, Qiyuan Tian1, Itthi Chatnuntawech2, Siddharth Srinivasan Iyer1, Stephen F Cauley1, Thorsten Feiweier3, Shivraman Giri4, Yuxin Hu5, Susie Y Huang1, Jonathan R Polimeni1, Lawrence L Wald1, and Kawin Setsompop1

1Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2National Nanotechnology Center, Pathum Thani, Thailand, 3Siemens Healthcare, Erlangen, Germany, 4Siemens Healthcare, Charlestown, MA, United States, 5Department of Electrical Engineering, Stanford University, Stanford, CA, United States

We introduce acquisition and reconstruction strategies for robust, high-quality multi-shot EPI (msEPI) without phase navigators. We extend the MUSSELS low-rank constrained parallel imaging technique to perform Virtual Coil (VC) reconstruction, and demonstrate diffusion imaging with sub-millimeter in-plane resolution using 55% partial-Fourier (PF) sampling. We propose Blip Up-Down Acquisition (BUDA) using interleaved blip-up and -down phase encoding, and incorporate B0 forward-modeling into MUSSELS to enable distortion- and navigator-free msEPI. We improve the acquisition efficiency by developing Simultaneous MultiSlice (SMS-)MUSSELS, and combine it with machine learning (ML) to provide Rtotal=16-fold acceleration with 3-shots. Deploying this in a spin-and-gradient-echo (SAGE) scan with signal modeling allows for whole-brain T2 and T2* mapping with high geometric fidelity in 12.5 seconds.

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