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 R_total=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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