ArticleEuropean radiology2020
Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning-based filter using convolutional neural network.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 43 citations in OpenAlex.
- Optimization of Respiratory Training Methods for Cardiac Magnetic Resonance Imaging.Journal of magnetic resonance imaging : JMRI · 2026Article
- Deep Learning-Based Reconstruction Improves Image Quality in Canine Cranial Abdominal MRI: A Prospective Pilot Study.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2026Article
- Deep Learning-based Multiple Arterial Phase MRI: A Step toward Improved Hepatocellular Carcinoma Diagnosis.Radiology. Imaging cancer · 2026Article
- Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI.Journal of imaging · 2026Article
- From promise to practice: a scoping review of AI applications in abdominal radiology.Abdominal radiology (New York) · 2026Article
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- Reducing respiratory artifacts in gadoxetic acid-enhanced magnetic resonance imaging via a patient-adapted breath-holding training strategy.Quantitative imaging in medicine and surgery · 2026Article
- Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.ArXiv · 2025Article
- Role of Artificial Intelligence in Reducing Error Rates in Radiology: A Scoping Review.Cureus · 2025Review
- Placenta segmentation redefined: review of deep learning integration of magnetic resonance imaging and ultrasound imaging.Visual computing for industry, biomedicine, and art · 2025Review
- A Fundamental Study on the Removal of Vascular Pulsation Artifacts Using U-Net-Based Deep Neural Network.Cureus · 2025Article
- Investigating the Use of Generative Adversarial Networks-Based Deep Learning for Reducing Motion Artifacts in Cardiac Magnetic Resonance.Journal of multidisciplinary healthcare · 2025Article
- Stop moving: MR motion correction as an opportunity for artificial intelligence.Magma (New York, N.Y.) · 2024Review
- AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis.Insights into imaging · 2023Article
- Evaluation of motion artefact reduction depending on the artefacts' directions in head MRI using conditional generative adversarial networks.Scientific reports · 2023Article
- Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.Journal of digital imaging · 2023Review
- Respiratory Motion Management in Abdominal MRI:Radiology · 2023Review
- Clinical validation of an AI-based motion correction reconstruction algorithm in cerebral CT.European radiology · 2022Article
- Faster and Better: How Anomaly Detection Can Accelerate and Improve Reporting of Head Computed Tomography.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium-enhanced multi-arterial phase MRI of the liver.
methodsThis retrospective study included 192 patients (131 men, 68.7 ± 10.3 years) receiving gadoxetate disodium-enhanced liver MRI in 2017. Datasets were submitted to a newly developed filter (MARC), consisting of 7 convolutional layers, and trained on 14,190 cropped images generated from abdominal MR images. Motion artifact for training was simulated by adding periodic k-space domain noise to the images. Original and filtered images of pre-contrast and 6 arterial phases (7 image sets per patient resulting in 1344 sets in total) were evaluated regarding motion artifacts on a 4-point scale. Lesion conspicuity in original and filtered images was ranked by side-by-side comparison.
resultsOf the 1344 original image sets, motion artifact score was 2 in 597, 3 in 165, and 4 in 54 sets. MARC significantly improved image quality over all phases showing an average motion artifact score of 1.97 ± 0.72 compared to 2.53 ± 0.71 in original MR images (p < 0.001). MARC improved motion scores from 2 to 1 in 177/596 (29.65%), from 3 to 2 in 119/165 (72.12%), and from 4 to 3 in 34/54 sets (62.96%). Lesion conspicuity was significantly improved (p < 0.001) without removing anatomical details.
conclusionsMotion artifacts and lesion conspicuity of gadoxetate disodium-enhanced arterial phase liver MRI were significantly improved by the MARC filter, especially in cases with substantial artifacts. This method can be of high clinical value in subjects with failing breath-hold in the scan. KEY POINTS: • This study presents a newly developed deep learning-based filter for artifact reduction using convolutional neural network (motion artifact reduction with convolutional neural network, MARC). • MARC significantly improved MR image quality after gadoxetate disodium administration by reducing motion artifacts, especially in cases with severely degraded images. • Postprocessing with MARC led to better lesion conspicuity without removing anatomical details.
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