ArticlePloS one2023
Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiographic evaluation of key anatomical markers in discoid lateral meniscus: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- Respiratory Motion Management in Abdominal MRI: Revisiting the Gap Between Technical Advances and Clinical Translation.Magnetic resonance in medicine · 2026Review
- Article
- Evaluation of the image quality index with MRI motion artifacts on tumor segmentation using deep learning.Radiological physics and technology · 2026Article
- Image quality evaluation of decubitus lumbar spine MRI using a flexible coil and PROPELLER sequence: comparison with conventional supine MRI.Skeletal radiology · 2026Article
- A Novel Method for Motion Blur Detection and Quantification Using Signal Analysis on a Controlled Empirical Image Dataset.Sensors (Basel, Switzerland) · 2026Article
- AgentMRI: A Vison Language Model-Powered AI System for Self-regulating MRI Reconstruction with Multiple Degradations.Journal of imaging informatics in medicine · 2026Article
- Optimizing high-resolution knee MRI at 3 tesla: conventional acceleration versus deep learning reconstruction.BMC medical imaging · 2026Article
- Magnetic resonance urography (MRU): technique mastery and illustrative case reviews.Abdominal radiology (New York) · 2026Review
- Domain-Adaptive MRI Learning Model for Precision Diagnosis of CNS Tumors.Biomedicines · 2026Article
- Development of a prediction method for severe pancreatitis using a nomogram.Frontiers in medicine · 2026Article
- AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts.European radiology · 2025Article
- Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.ArXiv · 2025Article
- Deep learning-driven abbreviated knee MRI protocols: diagnostic accuracy in clinical practice.La Radiologia medica · 2025Article
- Prostate MRI Using Deep Learning Reconstruction in Response to Cancer Screening Demands-A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.Tomography (Ann Arbor, Mich.) · 2025Article
- Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.AJNR. American journal of neuroradiology · 2025Article
- Investigating the Use of Generative Adversarial Networks-Based Deep Learning for Reducing Motion Artifacts in Cardiac Magnetic Resonance.Journal of multidisciplinary healthcare · 2025Article
- Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies.Scientific reports · 2024Article
- Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles of Datasets with Different Contrast Protocols and Liver Disease Etiologies.Research square · 2024Article
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Authors and funding
8 authors.
Funding
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Abstract
Motion artifacts deteriorate the quality of magnetic resonance (MR) images. This study proposes a new method to detect phase-encoding (PE) lines corrupted by motion and remove motion artifacts in MR images. 67 cases containing 8710 slices of axial T2-weighted images from the IXI public dataset were split into three datasets, i.e., training (50 cases/6500 slices), validation (5/650), and test (12/1560) sets. First, motion-corrupted k-spaces and images were simulated using a pseudo-random sampling order and random motion tracks. A convolutional neural network (CNN) model was trained to filter the motion-corrupted images. Then, the k-space of the filtered image was compared with the motion-corrupted k-space line-by-line, to detect the PE lines affected by motion. Finally, the unaffected PE lines were used to reconstruct the final image using compressed sensing (CS). For the simulated images with 35%, 40%, 45%, and 50% unaffected PE lines, the mean peak signal-to-noise ratio (PSNRs) of resulting images (mean±standard deviation) were 36.129±3.678, 38.646±3.526, 40.426±3.223, and 41.510±3.167, respectively, and the mean structural similarity (SSIMs) were 0.950±0.046, 0.964±0.035, 0.975±0.025, and 0.979±0.023, respectively. For images with more than 35% PE lines unaffected by motion, images reconstructed with proposed algorithm exhibited better quality than those images reconstructed with CS using 35% under-sampled data (PSNR 37.678±3.261, SSIM 0.964±0.028). It was proved that deep learning and k-space analysis can detect the k-space PE lines affected by motion and CS can be used to reconstruct images from unaffected data, effectively alleviating the motion artifacts.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.