用雷达数据生成心脏3D网格，实现无创精准建模
Towards 3D heart mesh generation using contactless radar imaging and physics-informed neural network
- 从粗到细变形模板网格，保持解剖连通性
- 新损失函数确保重建形状与原始雷达回波一致
- 首个大规模雷达-网格配对数据集，适合医疗影像研究
心脏功能评估需要持续、无创监测，而MRI存在局限。毫米波雷达及其合成孔径雷达（SAR）模式具备隐私保护和便携优势，适用于床旁临床应用。然而，从SAR图像重建高保真3D心脏几何仍具挑战：传统雷达方法生成稀疏点云，缺乏连续表面拓扑；直接应用光学重建网络因严重斑点噪声和边界模糊表现不佳。为此，我们提出SAR2Mesh框架，将任务重构为由粗到细的网格变形过程。通过初始拓扑模板，逐步变形以显式保持解剖连通性。引入几何感知特征投影模块，通过3D到2D采样提取多视角特征，并设计物理信息雷达损失，强制预测几何与原始雷达回波一致。此外，我们构建了首个大规模配对数据集Cardiac Mesh-SAR。大量实验表明，SAR2Mesh显著优于现有基于图像的基线方法，实现准确且物理一致的心脏重建。
原文摘要 · Abstract (English)
Cardiac function evaluation necessitates continuous, non-invasive monitoring, a capability limited in MRI. Millimeter-wave (mmWave) radar and its Synthetic Aperture Radar (SAR) mode offer a privacy-preserving and portable point-of-care clinical applications. However, reconstructing high-fidelity 3D cardiac geometry from SAR remains an open challenge. Traditional radar methods generate sparse point clouds that lack continuous surface topology. Meanwhile, direct application of optical reconstruction networks performs poorly due to the severe speckle noise and ambiguous boundaries inherent in SAR images. To bridge this gap, we propose SAR2Mesh, a novel framework that reformulates the task as a coarse-to-fine mesh deformation process. By initializing with a topological template, our approach explicitly preserves anatomical connectivity through progressive mesh deformation.We introduce a geometry-aware feature projection module to extract multi-view features via 3D-to-2D sampling, and a physics-informed radar loss to enforce consistency between the predicted geometry and raw radar echoes. Furthermore, we present Cardiac Mesh-SAR, the first large-scale paired SAR-mesh dataset. Extensive experiments demonstrate that SAR2Mesh significantly outperforms existing image-based baselines, achieving accurate and physically consistent cardiac reconstructions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。