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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2604.04477 |
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| _version_ | 1866914448162160640 |
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| author | Yao, Jincao Zhang, Ke Zhou, Yahan Shen, Jiafei Liu, Jie Ali, Mudassar Feng, Bojian Chen, Jiye Fan, Jinlong Liang, Ping Xu, Dong |
| author_facet | Yao, Jincao Zhang, Ke Zhou, Yahan Shen, Jiafei Liu, Jie Ali, Mudassar Feng, Bojian Chen, Jiye Fan, Jinlong Liang, Ping Xu, Dong |
| contents | Super-resolution ultrasound (SRUS) technology has overcome the resolution limitations of conventional ultrasound, enabling micrometer-scale imaging of microvasculature. However, due to the nature of imaging principles, three-dimensional reconstruction of microvasculature from SRUS remains an open challenge. We developed microvascular visualization fold (MVis-Fold), an innovative three-dimensional microvascular reconstruction model that integrates a cross-scale network architecture. This model can perform high-fidelity inference and reconstruction of three-dimensional microvascular networks from two-dimensional SRUS images. It precisely calculates key parameters in three-dimensional space that traditional two-dimensional SRUS cannot readily obtain. We validated the model's accuracy and reliability in three-dimensional microvascular reconstruction of solid tumors. This study establishes a foundation for three-dimensional quantitative analysis of microvasculature. It provides new tools and methods for diagnosis and monitoring of various diseases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04477 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MVis-Fold: A Three-Dimensional Microvascular Structure Inference Model for Super-Resolution Ultrasound Yao, Jincao Zhang, Ke Zhou, Yahan Shen, Jiafei Liu, Jie Ali, Mudassar Feng, Bojian Chen, Jiye Fan, Jinlong Liang, Ping Xu, Dong Computer Vision and Pattern Recognition Super-resolution ultrasound (SRUS) technology has overcome the resolution limitations of conventional ultrasound, enabling micrometer-scale imaging of microvasculature. However, due to the nature of imaging principles, three-dimensional reconstruction of microvasculature from SRUS remains an open challenge. We developed microvascular visualization fold (MVis-Fold), an innovative three-dimensional microvascular reconstruction model that integrates a cross-scale network architecture. This model can perform high-fidelity inference and reconstruction of three-dimensional microvascular networks from two-dimensional SRUS images. It precisely calculates key parameters in three-dimensional space that traditional two-dimensional SRUS cannot readily obtain. We validated the model's accuracy and reliability in three-dimensional microvascular reconstruction of solid tumors. This study establishes a foundation for three-dimensional quantitative analysis of microvasculature. It provides new tools and methods for diagnosis and monitoring of various diseases. |
| title | MVis-Fold: A Three-Dimensional Microvascular Structure Inference Model for Super-Resolution Ultrasound |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.04477 |