Keywords: Artifacts, Artifacts, susceptibility artifacts, echo planar imaging, reversed phase-encoding, deep learning, unsupervised learning
Motivation: Classical susceptibility-artifact correction methods are impractical in clinical settings given their computational burden.
Goal(s): Fast and effective correction of susceptibility artifacts in EPI via physics-driven unsupervised deep learning by utilizing phase-injected complex-valued forward-distortion.
Approach: Previous methods apply distortion correction on magnitude images, potentially yielding suboptimal performance near regions of signal dropout/pileup. We propose a novel model, compFD-Net, employing phase-injected complex forward-distortion that leverages a predicted phase image, additionally to the magnitude image and displacement field estimates, for improved capture of signal dropout/pileup artifacts in EPI images.
Results: The proposed model boosts susceptibility-artifact correction performance, notably improving predicted image and field quality.
Impact: Robust emulation of signal-dropout/pileup via the complex forward-distortion formulation boosts reliability in unsupervised artifact correction. compFD-Net facilitates rapid and performant correction of susceptibility artifacts in EPI, with possible impact in time-sensitive applications in clinical settings.
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