ArticleThe international journal of cardiovascular imaging2023
Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning.
Article in The international journal of cardiovascular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
6 citing papers in PubMed.
- Use of proper orthogonal decomposition and machine learning for efficient blood flow prediction in cerebral saccular aneurysms.Scientific reports · 2025Article
- Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review.Frontiers in cardiovascular medicine · 2025Review
- Generalized Super-Resolution 4D Flow MRI - Using Ensemble Learning to Extend Across the Cardiovascular System.IEEE journal of biomedical and health informatics · 2024Article
- Four-dimensional flow MRI for quantitative assessment of cerebrospinal fluid dynamics: Status and opportunities.NMR in biomedicine · 2024Review
- Revolutionizing vascular imaging: trends and future directions of 4D flow MRI based on a 20-year bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Generalized super-resolution 4D Flow MRI-using ensemble learning to extend across the cardiovascular system.ArXiv · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation (AR), a type of valvular heart disease. Metrics derived from blood flows are used to indicate AR onset and evaluate its severity. These metrics can be non-invasively obtained using four-dimensional (4D) flow magnetic resonance imaging (MRI), where accuracy is primarily dependent on spatial resolution. However, insufficient resolution often results from limitations in 4D flow MRI and complex aortic regurgitation hemodynamics. To address this, computational fluid dynamics simulations were transformed into synthetic 4D flow MRI data and used to train a variety of neural networks. These networks generated super-resolution, full-field phase images with an upsample factor of 4. Results showed decreased velocity error, high structural similarity scores, and improved learning capabilities from previous work. Further validation was performed on two sets of in vivo 4D flow MRI data and demonstrated success in de-noising flow images. This approach presents an opportunity to comprehensively analyse AR hemodynamics in a non-invasive manner.
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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.