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Three-dimensional (3D) freehand ultrasound (US) is a widely used imagingmodality that allows non-invasive imaging of medical anatomy without radiationexposure. The freehand 3D US surface reconstruction is vital to acquire theaccurate anatomical structures needed for modeling, registration, andvisualization. However, the currently used traditional methods cannot produce ahigh-quality surface due to imaging noise and connectivity issues in US.Although the deep learning-based approaches exhibiting the improvements insmoothness, continuity and resolution, the investigation into freehand 3D USremains limited. In this study, we introduce a self-supervised neural implicitsurface reconstruction method to learn the signed distance functions (SDFs)from freehand 3D US volumetric point clouds. In particular, our methoditeratively learns the SDFs by moving the 3D queries sampled around the pointclouds to approximate the surface with the assistance of two novel geometricconstraints. We assess our method on the three imaging systems, usingtwenty-three shapes that include six distinct anthropomorphic phantoms datasetsand seventeen in vivo carotid artery datasets. Experimental results on phantomsoutperform the existing approach, with a 67% reduction in Chamfer distance, 60%in Hausdorff distance, and 61% in Average absolute distance. Furthermore, ourmethod achieves a 0.92 Dice score on the in vivo datasets and demonstratesgreat clinical potential.

Neural Implicit Surface Reconstruction for Freehand 3D Ultrasound Volumetric Point Clouds with Geometric Constraints