ArticleBMC medical imaging2022
Motion artefact reduction in coronary CT angiography images with a deep learning method.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Image quality assessment of ECG-less coronary CT angiography: A comparative study with conventional ECG-gated CCTA.European journal of radiology open · 2026Article
- Acquisition-inspired motion artifact modeling and degradation-consistent diffusion restoration in photoacoustic microscopy.Photoacoustics · 2026Article
- Effect of ZeeFree algorithm on the reduction of stair-step artifacts for coronary computed tomography angiography.Scientific reports · 2026Article
- Advanced detection of coronary artery disease using a GAN-transformer model on plasma cytokine profiles.Scientific reports · 2026Article
- Assessment of Fractional Flow Reserve from Coronary CT Angiography Using a Deep Learning-Based Algorithm: A Multicenter Retrospective Study.Diagnostics (Basel, Switzerland) · 2026Article
- Thoracic-abdominopelvic contrast-enhanced dual-source computed tomography with a high-pitch acquisition protocol and free breathing for reducing motion artifacts and scan time in patients with cancer: a prospective study.Quantitative imaging in medicine and surgery · 2025Article
- Deep learning in CT image reconstruction and processing: techniques, performance evaluation, radiation dose, and future perspective.The British journal of radiology · 2025Review
- Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.Radiology · 2025Review
- Automatic identification of coronary stent in coronary calcium scoring CT using deep learning.Scientific reports · 2024Article
- Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm.Journal of imaging informatics in medicine · 2024Article
- Enhancing diagnostic performance and image quality in coronary CT angiography: Impact of SnapShot Freeze 2 algorithm across varied heart rates in stent patients.Journal of applied clinical medical physics · 2024Article
- Article
- Motion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks.Physics in medicine and biology · 2024Article
- Evaluation of the second-generation whole-heart motion correction algorithm (SSF2) used to demonstrate the aortic annulus on cardiac CT.Scientific reports · 2023Article
- Enhancing coronary artery plaque analysis via artificial intelligence-driven cardiovascular computed tomography.Therapeutic advances in cardiovascular diseaseReview
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7 authors.
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Abstract
backgroundThe aim of this study was to investigate the ability of a pixel-to-pixel generative adversarial network (GAN) to remove motion artefacts in coronary CT angiography (CCTA) images.
methodsNinety-seven patients who underwent single-cardiac-cycle multiphase CCTA were retrospectively included in the study, and raw CCTA images and SnapShot Freeze (SSF) CCTA images were acquired. The right coronary artery (RCA) was investigated because its motion artefacts are the most prominent among the artefacts of all coronary arteries. The acquired data were divided into a training dataset of 40 patients, a verification dataset of 30 patients and a test dataset of 27 patients. A pixel-to-pixel GAN was trained to generate improved CCTA images from the raw CCTA imaging data using SSF CCTA images as targets. The GAN's ability to remove motion artefacts was evaluated by the structural similarity (SSIM), Dice similarity coefficient (DSC) and circularity index. Furthermore, the image quality was visually assessed by two radiologists.
resultsThe circularity was significantly higher for the GAN-generated images than for the raw images of the RCA (0.82 ± 0.07 vs. 0.74 ± 0.11, p < 0.001), and there was no significant difference between the GAN-generated images and SSF images (0.82 ± 0.07 vs. 0.82 ± 0.06, p = 0.96). Furthermore, the GAN-generated images achieved the SSIM of 0.87 ± 0.06, significantly better than those of the raw images 0.83 ± 0.08 (p < 0.001). The results for the DSC showed that the overlap between the GAN-generated and SSF images was significantly higher than the overlap between the GAN-generated and raw images (0.84 ± 0.08 vs. 0.78 ± 0.11, p < 0.001). The motion artefact scores of the GAN-generated CCTA images of the pRCA and mRCA were significantly higher than those of the raw CCTA images (3 [4-3] vs 4 [5-4], p = 0.022; 3 [3-2] vs 5[5-4], p < 0.001).
conclusionsA GAN can significantly reduce the motion artefacts in CCTA images of the middle segment of the RCA and has the potential to act as a new method to remove motion artefacts in coronary CCTA images.
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