Keywords: Motion Correction, Motion Correction, Neuroimaging, AI
Motivation: Data-driven retrospective motion correction methods currently face challenges with respect to robustness and long runtimes, which can be addressed by combining deep learning- and physics-based methods.
Goal(s): To validate a novel deep learning-assisted joint estimation algorithm on real motion-corrupted 3D MRI data.
Approach: A dataset of motion-corrupted data was acquired on 4 healthy volunteers. The performance of the proposed method was compared to a state-of-the-art deep learning method and a physics-based method.
Results: The proposed method outperformed the deep learning- and physics-based methods, yielding better image correction and converging faster.
Impact: The proposed retrospective motion correction method can be adopted into clinical practice as an alternative to rescanning, having demonstrated that it can salvage real motion-corrupted data without special hardware and requiring minimal sequence modifications.
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