Towards 3D heart mesh generation using contactless radar imaging and physics-informed neural network
This work enables non-invasive, continuous cardiac monitoring using portable radar, addressing a key limitation of MRI for point-of-care applications.
The authors tackle the challenge of reconstructing high-fidelity 3D cardiac geometry from millimeter-wave radar SAR images. They propose SAR2Mesh, a coarse-to-fine mesh deformation framework with a physics-informed radar loss, achieving accurate and physically consistent cardiac reconstructions, significantly outperforming image-based baselines.
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.