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Abstract #0224

Robust Multi-shot EPI with Untrained Artificial Neural Networks: Unsupervised Scan-specific Deep Learning for Blip Up-Down Acquisition (BUDA)

Tae Hyung Kim1,2,3, Zijing Zhang1,2,4, Jaejin Cho1,2, Borjan Gagoski2,5, Justin Haldar3, and Berkin Bilgic1,2
1Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States, 2Radiology, Harvard Medical School, Boston, MA, United States, 3Electrical Engineering, University of Southern California, Los Angeles, CA, United States, 4State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China, 5Boston Children's Hospital, Boston, MA, United States

Blip Up-Down Acquisition (BUDA) has been successful in generating distortion-free multi-shot EPI (msEPI) without navigators, utilizing a fieldmap and structured low-rank constraints. Recently, a scan-specific artificial neural network (ANN) motivated by structured low-rank modeling, named LORAKI, has been proposed for refined MRI reconstruction, where its training employed fully-sampled autocalibrated signal (ACS). Although applying LORAKI framework to BUDA is beneficial, acquiring fully-sampled ACS for msEPI is not practical. We propose scan-specific unsupervised ANNs for improved BUDA msEPI without training data. Experiment results indicate that the proposed BUDA-LORAKI exhibits advantages, with up to 1.5x reduction in NRMSE compared to standard BUDA reconstruction.

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