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Abstract #0670

An Unsupervised Deep Learning Method for Correcting the Susceptibility Artifacts in Reversed Phase-encoding EPIs

Soan Thi Minh Duong1, Sui Paul Ang1, and Mark Matthias Schira2
1School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, Australia, 2School of Psychology, University of Wollongong, Wollongong, Australia

We introduce a deep learning method, named S-Net, to correct the susceptibility artifacts in a pair of reversed phase-encoding (PE) echo-planar imaging images. The S-Net is trained in an unsupervised manner using a set of reversed-PE pairs. For a new reversed-PE pair, the corrected images are computed rapidly by evaluating the learned S-Net. Evaluation of three datasets demonstrates equally good correction performance as much lower computation time (1-3s) than state-of-the-art SAC methods such as AISAC (50-60s) or TOPUP (over 1000s). This fast performance provides a dramatic speedup for medical imaging processing pipelines and makes the real-time correction for MR-scanners feasible.

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