Self-supervised resolution enhancement for anisotropic volumes in edge illumination X-ray phase contrast micro-computed tomography

Abstract

X-ray phase contrast micro-computed tomography (micro-CT) can achieve higher contrast than conventional absorption-based X-ray micro-CT by utilizing refraction in addition to attenuation. In this work, we focus on a specific X-ray phase contrast technique, edge illumination (EI) micro-CT. EI uses a sample mask with transmitting apertures that split the X-ray beam into narrow beamlets, enabling detection of refraction-included intensity variations. Between the typical mask designs (circular and slit-shaped apertures), slit-shaped apertures offer practical advantages over circular ones, as they only require sample stepping in one direction, thereby reducing scanning time. However, this leads to anisotropic resolution, as the slit-shaped apertures enhances resolution only along the direction orthogonal to the slits. To address this limitation, we propose a self-supervised method that trains on high-resolution in-plane images to enhance resolution for out-of-plane images, effectively mitigating anisotropy. Our results on both simulated and real EI micro-CT datasets demonstrate the effectiveness of the proposed method.