Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
561-02-008 ISMRM Abstract

Automated Image Quality Evaluation of Cine Cardiac MRI Using a Convolutional Neural Network

Primary: Cardiovascular
Secondary: Acquisition & Reconstruction - Artifacts and Correction Strategies
561-02-008 · Classification and Analysis in the Body · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row B
Keywords: MRI Workflow optimzation Deep learning Image Quality Assessment Artifacts correction Cardiovascular magnetic resonance
Accepted
Limin Zhou 1, Omer B Demirel1, Josh Greer2, Melvyn B Ooi1, Andrew J Powell3,4
1Philips North America Clinical Science, Rochester, United States of America
2Philips, Cincinnati, United States of America
3Department of Cardiology, Boston Children's Hospital and Harvard Medical School, Boston, United States of America
4Department of Pediatrics, Harvard Medical School, Boston, United States of America
Presenting Author: Limin Zhou

Synopsis

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References

1. Ferreira, P.F. et al. (2013) “Cardiovascular magnetic resonance artefacts,” Journal of Cardiovascular Magnetic Resonance, 15(1), p. 41. Available at: https://doi.org/10.1186/1532-429X-15-41. [doi]
2. Oksuz, I. et al. (2019) “Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning,” Medical Image Analysis, 55, pp. 136–147. Available at: https://doi.org/10.1016/j.media.2019.04.009. [doi]
3. Piccini, D. et al. (2020) “Deep Learning to Automate Reference-Free Image Quality Assessment of Whole-Heart MR Images,” Radiology. Artificial Intelligence, 2(3), p. e190123. Available at: https://doi.org/10.1148/ryai.2020190123. [doi]
4. He, K. et al. (2015) “Deep Residual Learning for Image Recognition.” arXiv. Available at: https://doi.org/10.48550/arXiv.1512.03385. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/561-02-008