ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024
ROBUST OUTER VOLUME SUBTRACTION WITH DEEP LEARNING GHOSTING DETECTION FOR HIGHLY-ACCELERATED REAL-TIME DYNAMIC MRI.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- REVISITING MRI RECONSTRUCTION USING A COMBINATION OF COMPLEX AND MAGNITUDE MEASUREMENTS WITH LEARNED PRIORS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI.ArXiv · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Real-time dynamic MRI is important for visualizing time-varying processes in several applications, including cardiac imaging, where it enables free-breathing images of the beating heart without ECG gating. However, current real-time MRI techniques commonly face challenges in achieving the required spatio-temporal resolutions due to limited acceleration rates. In this study, we propose a deep learning (DL) technique for improving the estimation of stationary outer-volume signal from shifted time-interleaved undersampling patterns. Our approach utilizes the pseudo-periodic nature of the ghosting artifacts arising from the moving organs. Subsequently, this estimated outer-volume signal is subtracted from individual timeframes of the real-time MR time series, and each timeframe is reconstructed individually using physics-driven DL methods. Results show improved image quality at high acceleration rates, where conventional methods fail.
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Registered trials
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