Application of Noise2Inverse and adaptation (Noise2Phase) to single‐mask x‐ray phase contrast micro‐computed tomography

X-ray phase contrast imaging (XPCI), when implemented in micro-computed tomography (micro-CT) mode, offers high-contrast 3D imaging of weakly-attenuating material samples. In the so-called single-mask edge illumination approach, a mask with periodically spaced transmitting apertures is used to split the x-ray beam into narrow beamlets; when the beamlets are aligned with the boundaries ('edges') between detector pixels, their refraction-induced deviation can be detected and used to form images. A shortcoming is that the mask reduces the x-ray flux, necessitating longer exposures and therefore longer acquisition times. We show that the demand on exposure time can be relaxed by integrating the deep learning-based denoising technique Noise2Inverse into the image processing workflow. The applicability of Noise2Inverse to single-mask edge illumination XPCI micro-CT is demonstrated, and its performance at severe noise levels is explored. Taking advantage of the distinct imaging system characteristics, we also propose an adaptation of Noise2Inverse, called Noise2Phase, which does not rely on splitting the CT dataset by projections.