# Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation

arXiv: https://arxiv.org/abs/1910.05370 · HTML: https://mlsanity.com/paper/1910.05370/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 18 of 18)

- [Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.12215/) (2022)
- [Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review](https://mlsanity.com/paper/2305.06739/) (2023)
- [A review and experimental evaluation of deep learning methods for MRI reconstruction](https://mlsanity.com/paper/2109.08618/) (2021)
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- [Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation](https://mlsanity.com/paper/2202.09798/) (2022)
- [unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features](https://mlsanity.com/paper/2308.15507/) (2023)
- [CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR](https://mlsanity.com/paper/2507.02289/) (2025)
- [Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction](https://mlsanity.com/paper/2509.05071/) (2025)
- [Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation](https://mlsanity.com/paper/2209.09678/) (2022)
- [Joint Calibrationless Reconstruction and Segmentation of Parallel MRI](https://mlsanity.com/paper/2105.09220/) (2021)
- [Explainable Image Quality Assessment for Medical Imaging](https://mlsanity.com/paper/2303.14479/) (2023)
- [Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI](https://mlsanity.com/paper/2405.04974/) (2024)
- [Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI](https://mlsanity.com/paper/2405.10570/) (2024)
- [SIRe-Networks: Convolutional Neural Networks Architectural Extension for Information Preservation via Skip/Residual Connections and Interlaced Auto-Encoders](https://mlsanity.com/paper/2110.02776/) (2021)
- [A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis](https://mlsanity.com/paper/2205.01673/) (2022)
- [Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learning](https://mlsanity.com/paper/2502.17209/) (2025)
- [Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion](https://mlsanity.com/paper/2610.06094/) (2026)

## Abstract (arXiv)

Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependent on the quality of the image that is being segmented. A commonly neglected point in the medical image analysis community is the vast amount of clinical images that have severe image artefacts due to organ motion, movement of the patient and/or image acquisition related issues. In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity. The method is based on our recently developed joint artefact detection and reconstruction method, which reconstructs high quality MR images from k-space using a joint loss function and essentially converts the artefact correction task to an under-sampled image reconstruction task by enforcing a data consistency term. In this paper, we propose to use a segmentation network coupled with this in an end-to-end framework. Our training optimises three different tasks: 1) image artefact detection, 2) artefact correction and 3) image segmentation. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted cardiac MR k-space data and uncorrected reconstructed images. Using a test set of 500 2D+time cine MR acquisitions from the UK Biobank data set, we achieve demonstrably good image quality and high segmentation accuracy in the presence of synthetic motion artefacts. We showcase better performance compared to various image correction architectures.