UCLA

Micro-ultrasound (MicroUS) is a novel imaging technology with the potential to provide a low-cost and high-resolution approach for prostate cancer diagnosis. However, MicroUS is acquired in a non-uniform, fan-shaped sweep, where voxel size varies with distance from the probe and across slice angles. This irregular voxel distribution complicates reformatting into other imaging planes, making it challenging to conduct joint evaluations with other modalities such as MRI and histopathology. Existing interpolation-based reformatting methods lead to poor image resolution and introduce severe artifacts. In this paper, we propose MPR-Diff, a self-supervised diffusion model for super-resolution-based multi-planar reformation in prostate MicroUS imaging. Our method addresses the lack of high-resolution reference in the target plane by extracting simulated training patches from acquired slices. We performed both a quantitative evaluation and an expert reader study, demonstrating that our approach significantly enhances image resolution and reduces artifacts, thereby increasing the potential diagnostic value of MicroUS. Code is available at https://github.com/Calvin-Pang/MPR-Diff.