Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model. Issue 4 (2nd May 2019)

• Record Type: Journal Article
• Authors: Haskell, Melissa W.; Cauley, Stephen F.; Bilgic, Berkin; Hossbach, Julian; Splitthoff, Daniel N.; Pfeuffer, Josef; Setsompop, Kawin; Wald, Lawrence L.
• Is Part Of: Magnetic resonance in medicine. Volume 82:Issue 4(2019)
• Page Start: 1452
• Page End: 1461
• Publication Date: 2019-05-02
• Subjects: convolutional neural networks -- deep learning -- image reconstruction -- machine learning -- magnetic resonance -- motion correction; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27771
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: We introduce and validate a scalable retrospective motion correction technique for brain imaging that incorporates a machine learning component into a model‐based motion minimization. Methods: A convolutional neural network (CNN) trained to remove motion artifacts from 2D T2 ‐weighted rapid acquisition with refocused echoes (RARE) images is introduced into a model‐based data‐consistency optimization to jointly search for 2D motion parameters and the uncorrupted image. Our separable motion model allows for efficient intrashot (line‐by‐line) motion correction of highly corrupted shots, as opposed to previous methods which do not scale well with this refinement of the motion model. Final image generation incorporates the motion parameters within a model‐based image reconstruction. The method is tested in simulations and in vivo motion experiments of in‐plane motion corruption. Results: While the convolutional neural network alone provides some motion mitigation (at the expense of introduced blurring), allowing it to guide the iterative joint‐optimization both improves the search convergence and renders the joint‐optimization separable. This enables rapid mitigation within shots in addition to between shots. For 2D in‐plane motion correction experiments, the result is a significant reduction of both image space root mean square error in simulations, and a reduction of motion artifacts in the in vivo motion tests. Conclusion: The separability and convergence improvements afforded by the combined convolutional neural network+model‐based method shows the potential for meaningful postacquisition motion mitigation in clinical MRI.