Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding
This addresses motion correction in MRI for medical imaging, offering improved accuracy and efficiency, though it appears incremental as it builds on existing CG-SENSE and GAN methods.
The paper tackles motion artifacts in multishot MRI by proposing a generative network-based CG-SENSE reconstruction framework, which significantly outperforms state-of-the-art techniques in robustness and reduces computational times severalfold.
Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality that can produce a high-resolution image with relatively less data acquisition time. The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis. Numerous efforts have been made to address this issue; however, all of these proposals are limited in terms of how much motion they can correct and the required computational time. In this paper, we propose a novel generative networks based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. The proposed framework first employs CG-SENSE reconstruction to produce the motion-corrupted image and then a generative adversarial network (GAN) is used to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establishes that the proposed method significantly robust and outperforms state-of-the-art motion correction techniques and also reduces severalfold of computational times.