RVDeformer: Sparse Point Cloud-Guided Right Ventricle 3-D Reconstruction in Echocardiograms

Abstract

3D reconstruction of the Right Ventricle (RV) from echocardiograms is crucial for accurate clinical evaluation of cardiac function. However, existing methods are hindered by the complex RV anatomy and the incomplete spatial information inherent in 2D multi-view echocardiograms. Therefore, we propose RVDeformer, a sparse point cloud-guided framework for RV 3D reconstruction. RVDeformer reformulates the reconstruction task as a mesh deformation problem, learning to deform a predefined template mesh to match the target structure under the guidance of the sparse anatomical point cloud. Specifically, this framework employs the end-to-end neural network RVDeformNet to extract the features of the point cloud and template mesh for predicting the displacement of each mesh vertex. We design a point cloud-mesh fusion module that can effectively align and fuse features from the two modalities to enhance the representation ability of the model. We conduct extensive validation on a clinical dataset of 1,278 cases and demonstrate that RVDeformer outperforms existing state-of-the-art methods, achieving a Chamfer Distance (CD) of $2.24\pm 0.55$ mm, an F1-score of $0.74\pm 0.10$ at the 3 mm threshold, and a Volumetric Similarity (VS) of $91.53\pm 2.28$ %, with significant potential for clinical applications. The code is available at https://github.com/onezh95/RVDeformer