Deep Learning-Assisted Joint Estimation for 3D Retrospective Motion Correction: An In-Vivo Validation

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.