Micro-ultrasound super-resolution with geometry-driven consistency models

Prostate cancer diagnosis typically relies on image-guided biopsy. Micro-ultrasound (MicroUS) has recently emerged as a promising imaging modality, offering high spatial resolution with comparable diagnostic performance to multi-parametric MRI (mpMRI) at a lower cost. However, unlike MRI, MicroUS volumes are acquired in a fan-shaped, angular geometry, making direct alignment and comparison across imaging modalities challenging. High-quality multi-planar reformation (MPR) is therefore expected in treatment planning, yet conventional MPR produces blurred images with slice discontinuities. In this paper, we present UltraCCM, a fully self-supervised, geometry-driven conditional consistency model for super-resolution of reformatted MicroUS images. UltraCCM explicitly incorporates the geometric characteristics of MicroUS acquisition and formulates MPR as an angular super-resolution problem, enabling fast, single-step recovery of fine anatomical details without requiring high-resolution target-plane supervision. Extensive experiments on in vivo and ex vivo datasets, including quantitative evaluation, expert reader studies, and a downstream prostate segmentation task, demonstrate that UltraCCM improves perceptual quality and fine-detail visualization while preserving anatomy-relevant information for downstream image analysis. The code of UltraCCM is available at: https://github.com/Calvin-Pang/UltraCCM.