Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction
Not on the map. Why?
Abstract (arXiv)
Background: To systematically review and perform a meta-analysis of artificial intelligence (AI)-driven methods for detecting and correcting magnetic resonance imaging (MRI) motion artifacts, assessing current developments, effectiveness, challenges, and future research directions. Methods: A comprehensive systematic review and meta-analysis were conducted, focusing on deep learning (DL) approaches, particularly generative models, for the detection and correction of MRI motion artifacts. Quantitative data were extracted regarding utilized datasets, DL architectures, and performance metrics. Results: DL, particularly generative models, show promise for reducing motion artifacts and improving image quality; however, limited generalizability, reliance on paired training data, and risk of visual distortions remain key challenges that motivate standardized datasets and reporting. Conclusions: AI-driven methods, particularly DL generative models, show significant potential for improving MRI image quality by effectively addressing motion artifacts. However, critical challenges must be addressed, including the need for comprehensive public datasets, standardized reporting protocols for artifact levels, and more advanced, adaptable DL techniques to reduce reliance on extensive paired datasets. Addressing these aspects could substantially enhance MRI diagnostic accuracy, reduce healthcare costs, and improve patient care outcomes.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 37 of its 158 references that are in the map. First posted on arXiv 5 Sep 2025.
Builds on
37 of 158 references.
References · 37
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
- 2022Generative Adversarial Networkscited by 5,603
- 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
- 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
- 2023Vision-Language Models for Vision Tasks: A Surveycited by 834
- 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 657
- 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
- 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
25 more
- 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 211
- 2020TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learningcited by 176
- 2018Distribution Matching Losses Can Hallucinate Features in Medical Image Translationcited by 99
- 2018CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)cited by 81
- 2018GANs for Medical Image Analysiscited by 81
- 2020On hallucinations in tomographic image reconstructioncited by 66
- 2018Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learningcited by 21
- 2023Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Reviewcited by 18
- 2019Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentationcited by 17
- 2021Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRIcited by 14
- 2021Learning MRI Artifact Removal With Unpaired Datacited by 13
- 2022Accelerated Motion Correction with Deep Generative Diffusion Modelscited by 12
- 2019Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imagingcited by 10
- 2025MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shiftingcited by 10
- 2024Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstructioncited by 9
- 2018Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the livercited by 7
- 2018Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Networkcited by 7
- 2020Iterative Motion Compensation reconstruction ultra-short TE(iMoCo UTE) for high resolution free breathing pulmonary MRIcited by 6
- 2023Physics-Aware Motion Simulation for T2*-Weighted Brain MRIcited by 4
- 2023SISMIK for brain MRI: Deep-learning-based motion estimation and model-based motion correction in k-spacecited by 4
- 2022Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolutioncited by 3
- 2023Motion-robust free-running volumetric cardiovascular MRIcited by 3
- 2020Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniquescited by 2
- 2023Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrastcited by 2
- 2022Motion correction in MRI using deep learning and a novel hybrid loss functioncited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 2.
3 papers.