Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915096034279424 |
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| author | Yang, Guang Chen, Jingkun Sheng, Xicheng Yang, Shan Zhuang, Xiahai Raman, Betty Li, Lei Grau, Vicente |
| author_facet | Yang, Guang Chen, Jingkun Sheng, Xicheng Yang, Shan Zhuang, Xiahai Raman, Betty Li, Lei Grau, Vicente |
| contents | Late gadolinium enhancement MRI (LGE MRI) is the gold standard for the detection of myocardial scars for post myocardial infarction (MI). LGE MRI requires the injection of a contrast agent, which carries potential side effects and increases scanning time and patient discomfort. To address these issues, we propose a novel framework that combines cardiac motion observed in cine MRI with image texture information to segment the myocardium and scar tissue in the left ventricle. Cardiac motion tracking can be formulated as a full cardiac image cycle registration problem, which can be solved via deep neural networks. Experimental results prove that the proposed method can achieve scar segmentation based on non-contrasted cine images with comparable accuracy to LGE MRI. This demonstrates its potential as an alternative to contrast-enhanced techniques for scar detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion Yang, Guang Chen, Jingkun Sheng, Xicheng Yang, Shan Zhuang, Xiahai Raman, Betty Li, Lei Grau, Vicente Image and Video Processing Computer Vision and Pattern Recognition Late gadolinium enhancement MRI (LGE MRI) is the gold standard for the detection of myocardial scars for post myocardial infarction (MI). LGE MRI requires the injection of a contrast agent, which carries potential side effects and increases scanning time and patient discomfort. To address these issues, we propose a novel framework that combines cardiac motion observed in cine MRI with image texture information to segment the myocardium and scar tissue in the left ventricle. Cardiac motion tracking can be formulated as a full cardiac image cycle registration problem, which can be solved via deep neural networks. Experimental results prove that the proposed method can achieve scar segmentation based on non-contrasted cine images with comparable accuracy to LGE MRI. This demonstrates its potential as an alternative to contrast-enhanced techniques for scar detection. |
| title | Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.05241 |