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MVis-Fold: A Three-Dimensional Microvascular Structure Inference Model for Super-Resolution Ultrasound

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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.

Jincao Yao, Ke Zhang, Yahan Zhou, Jiafei Shen, Jie Liu, Mudassar Ali, Bojian Feng, Jiye Chen, Jinlong Fan, Ping Liang, Dong Xu (1, 2, 3, 4) __INSTITUTION_11__ Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China, (2) Research Center of Interventional Medicine, Engineering, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China, (3) Wenling Institute of Big Data, Artificial Intelligence in Medicine, Taizhou, China, (4) Zhejiang Provincial Research Center for Innovative Technology, Equipment in Interventional Oncology, Zhejiang Cancer Hospital, Hangzhou, China, (5) College of Information Science, Electronic Engineering, Zhejiang University, Hangzhou, China, (6) Department of Ultrasound, Chinese PLA General Hospital, Chinese PLA Medical School, Beijing, China)• 2026

Related benchmarks

TaskDatasetResultRank
Mean Diameter EstimationMicrovascular Pathological Gold Standard
Mean Error (μm)2.16
4
Vascular SegmentationSRUS (test)
Dice Coefficient95.9
4
Vessel Density EstimationMicrovascular Pathological Gold Standard
Mean Error (mm/mm³)0.012
4
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