# Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding

arXiv: https://arxiv.org/abs/1902.07430 · HTML: https://mlsanity.com/paper/1902.07430/

## Key facts

- Cited by 14 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 20 Feb 2019.

## Built on by (most cited first, 14 of 14)

- [Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review](https://mlsanity.com/paper/2305.06739/) (2023)
- [Accelerated Motion Correction with Deep Generative Diffusion Models](https://mlsanity.com/paper/2211.00199/) (2022)
- [Wide Range MRI Artifact Removal with Transformers](https://mlsanity.com/paper/2210.07976/) (2022)
- [MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI](https://mlsanity.com/paper/2409.09370/) (2024)
- [Anatomy-Guided Representation Learning Using a Transformer-Based Network for Thyroid Nodule Segmentation in Ultrasound Images](https://mlsanity.com/paper/2512.12662/) (2025)
- [A Path Towards Clinical Adaptation of Accelerated MRI](https://mlsanity.com/paper/2208.12835/) (2022)
- [Motion correction in MRI using deep learning and a novel hybrid loss function](https://mlsanity.com/paper/2210.14156/) (2022)
- [Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs](https://mlsanity.com/paper/2303.17239/) (2023)
- [JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model](https://mlsanity.com/paper/2310.09625/) (2023)
- [Complex Swin Transformer for Accelerating Enhanced SMWI Reconstruction](https://mlsanity.com/paper/2512.22202/) (2025)
- [Highly efficient non-rigid registration in k-space with application to cardiac Magnetic Resonance Imaging](https://mlsanity.com/paper/2410.18834/) (2024)
- [MAISY: Motion-Aware Image SYnthesis for Medical Image Motion Correction](https://mlsanity.com/paper/2505.04105/) (2025)
- [Retrospective motion correction in MRI using disentangled embeddings](https://mlsanity.com/paper/2511.08365/) (2025)
- [Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI](https://mlsanity.com/paper/2512.20436/) (2025)

## Abstract (arXiv)

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.