Geometry-Aware Implicit Neural Reconstruction of Oblique Micro-Ultrasound Scans

Abstract

Micro-ultrasound is a high-resolution, low-cost modality for prostate cancer imaging, but its transrectal acquisition produces oblique slices at irregular angular intervals that lack a consistent anatomical frame of reference. We present a geometry-aware reconstruction framework: a coordinate-based sampling scheme that uses cylindrical acquisition geometry to accurately map each target voxel, combined with a generalizable implicit neural representation trained across thousands of scans to model the continuous intensity field between oblique slices. Our method achieves a 9% relative SSIM improvement over a coordinate-matched trilinear baseline while preserving ultrasound-specific speckle texture and boundary sharpness, and reduces reconstruction time by over 60% compared to per-scene optimization methods.