[论文解读] Scaling Ultrasound Volumetric Reconstruction via Mobile Augmented Reality

Kian Wei Ng, Yujia Gao+8 more|arXiv (Cornell University)|Feb 17, 2026

Digital Radiography and Breast Imaging被引用 0

一句话总结

MARVUS 将 2D 超声与移动端 AR 融合，使用低成本标定幻影和边缘分割重建 3D 结节，提升体积准确性并降低操作员间差异。

ABSTRACT

Accurate volumetric characterization of lesions is essential for oncologic diagnosis, risk stratification, and treatment planning. While imaging modalities such as Computed Tomography provide high-quality 3D data, 2D ultrasound (2D-US) remains the preferred first-line modality for breast and thyroid imaging due to cost, portability, and safety factors. However, volume estimates derived from 2D-US suffer from high inter-user variability even among experienced clinicians. Existing 3D ultrasound (3D-US) solutions use specialized probes or external tracking hardware, but such configurations increase costs and diminish portability, constraining widespread clinical use. To address these limitations, we present Mobile Augmented Reality Volumetric Ultrasound (MARVUS), a resource-efficient system designed to increase accessibility to accurate and reproducible volumetric assessment. MARVUS is interoperable with conventional ultrasound (US) systems, using a foundation model to enhance cross-specialty generalization while minimizing hardware requirements relative to current 3D-US solutions. In a user study involving experienced clinicians performing measurements on breast phantoms, MARVUS yielded a substantial improvement in volume estimation accuracy (mean difference: 0.469 cm3) with reduced inter-user variability (mean difference: 0.417 cm3). Additionally, we prove that augmented reality (AR) visualizations enhance objective performance metrics and clinician-reported usability. Collectively, our findings suggests that MARVUS can enhance US-based cancer screening, diagnostic workflows, and treatment planning in a scalable, cost-conscious, and resource-efficient manner. Usage video demonstration available (https://youtu.be/m4llYcZpqmM).

研究动机与目标

• 降低成本并提升跨临床领域的 3D 超声体积重建的可扩展性。
• 开发适用于标准 2D-US 系统的移动端 AR 工作流，所需额外硬件有限。
• 提高结节测量的体积准确性并降低操作员间的变异性。
• 提供 AR 可视化，增强临床医生对基于超声的工作流的信任和易用性。

提出的方法

• 通过一个新型单件 US 幻影进行单帧空间标定的标定方法。
• 使用探头、幻影和相机上的 ArUco 标记进行外部标定，以推导 T_US_Probe 及相关变换。
• 通过多相机位姿跟踪和固定参考系，将自由手工 2D-US 扫描重建为带纹理的 3D 点云。
• 使用 EdgeTAM 的多帧传播实现半自动结节分割，获得密集点云。
• 体素化和 marching cubes 以生成用于体积估计和 AR 可视化的网格。
• 通过将重建网格与实时超声数据叠加并显示网格-图像相交，来评估重建质量的 AR 验证。

实验结果

研究问题

• RQ1 相比标准手动椭圆体估计，MARVUS 是否能提高基于超声的体积测量的准确性？
• RQ2 AR 可视化是否能够降低 3D-US 结节体积估计中的操作员间变异性？
• RQ3 所提出的标定与重建工作流在常见的超声深度与增益下是否鲁棒？
• RQ4 整合 AR 可视化是否提升移动 3D-US 系统的用户信任与感知易用性？

主要发现

• 在专家级别测试中，MARVUS 相对于对照方法显著提升了体积估计的准确性（基线对比的具体数值已报告）。
• 与 Recon 单独相比，AR 增强工作流进一步降低了体积误差和操作员间变异性。
• 所提的单帧、基于标记的幻影标定重复性可与多种线缆基方法相比拟甚至更好，同时避免了对专门硬件的依赖。
• 参与者在使用带 AR 可视化的 MARVUS 时报告信心更高，系统可用性在有 AR 情境下也表现出温和提升。