ArticleIEEE transactions on medical imaging2021
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 83 citations in OpenAlex.
- A Novel Method for Motion Blur Detection and Quantification Using Signal Analysis on a Controlled Empirical Image Dataset.Sensors (Basel, Switzerland) · 2026Article
- Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI.Journal of imaging · 2026Article
- Cardiac multi-structure segmentation network based on the fused dual attention mechanism.Medical & biological engineering & computing · 2026Article
- Data-Driven System Matrix Manipulation Enabling Fast Functional Imaging in Tomography.IEEE transactions on medical imaging · 2026Article
- Movement Artifact Direction Estimation Based on Signal Processing Analysis of Single-Frame Images.Sensors (Basel, Switzerland) · 2025Article
- Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.ArXiv · 2025Article
- Complex-valued neural networks to speed-up MR thermometry during hyperthermia using Fourier PD and PDUNet.Scientific reports · 2025Article
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- Investigating the Use of Generative Adversarial Networks-Based Deep Learning for Reducing Motion Artifacts in Cardiac Magnetic Resonance.Journal of multidisciplinary healthcare · 2025Article
- Artificial Intelligence based fractional flow reserve.Cardiology journal · 2025Review
- Article
- Comprehensive review of artifacts in cardiac MRI and their mitigation.The international journal of cardiovascular imaging · 2024Review
- Stop moving: MR motion correction as an opportunity for artificial intelligence.Magma (New York, N.Y.) · 2024Review
- Motion Artifact Reduction Using U-Net Model with Three-Dimensional Simulation-Based Datasets for Brain Magnetic Resonance Images.Bioengineering (Basel, Switzerland) · 2024Article
- Joint suppression of cardiac bSSFP cine banding and flow artifacts using twofold phase-cycling and a dual-encoder neural network.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Article
- The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review.Biosensors · 2023Review
- Generative learning approach for radiation dose reduction in X-ray guided cardiac interventions.Medical physics · 2022Article
- Suppression of artifact-generating echoes in cine DENSE using deep learning.Magnetic resonance in medicine · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.
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Registered trials
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.