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

Harrison Chojnowski, Gorkem Can Ates, Wayne Grover Brisbane, Wei Shao

Keywords: micro-ultrasound, implicit neural representation, prostate, volume reconstruction

Abstract:

Micro-ultrasound is a new modality for accurate, low-cost prostate cancer imaging, but its acquisition produces oblique slices that do not align with axial MRI or histopathology. This geometric mismatch complicates interpretation and prevents direct registration to histopathology, which is necessary to map ground-truth cancer outlines onto micro-ultrasound for training machine learning models for automated cancer detection.
We address this challenge with a geometry-aware reconstruction framework that converts oblique micro-ultrasound slices into axial 3D volumes. Our method includes: (i) a coordinate-based sampling scheme that uses cylindrical geometry to accurately map each voxel into Cartesian space, and (ii) a generalized implicit neural representation that models the continuous intensity field between slices, preserving high-frequency speckle texture that traditional interpolation blurs. The reconstructed volumes achieve a 9\% relative SSIM improvement over a coordinate-matched trilinear baseline while maintaining ultrasound-specific texture and boundary detail. This framework produces high-quality axial micro-ultrasound volumes suitable for reliable histopathology registration and for creating pathology-informed datasets to train cancer detection models.

Primary Subject Area: Image Acquisition and Reconstruction

Secondary Subject Area: Geometric Deep Learning

Registration Requirement: Yes

Reproducibility: https://github.com/mirthAI/GeometryAware-MicroUS-INR

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 308