MVis-Fold: A Three-Dimensional Microvascular Structure Inference Model for Super-Resolution Ultrasound
This work addresses a critical bottleneck in medical imaging for diagnosing and monitoring diseases by enabling 3D quantitative analysis of microvasculature, though it appears incremental as it builds on existing super-resolution ultrasound technology.
The paper tackled the challenge of three-dimensional reconstruction of microvascular networks from two-dimensional super-resolution ultrasound images, developing MVis-Fold, a model that enables high-fidelity inference and precise calculation of key 3D parameters, validated in solid tumors.
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.