The diagnostic quality of computed tomography (CT) scans is usually restricted by the induced patient dose, scan speed, and image quality. Sparse-angle tomographic scans reduce radiation exposure and accelerate data acquisition, but suffer from image artifacts and noise. Existing image processing algorithms can restore CT reconstruction quality but often require large training data sets or can not be used for truncated objects. This work presents a self-supervised projection inpainting method that allows optimizing missing projective views via gradient-based optimization. By reconstructing independent stacks of projection data, a self-supervised loss is calculated in the CT image domain and used to directly optimize projection image intensities to match the missing tomographic views constrained by the projection geometry. Our experiments on real X-ray microscope (XRM) tomographic mouse tibia bone scans show that our method improves reconstructions by 3.1-7.4%/7.7-17.6% in terms of PSNR/SSIM with respect to the interpolation baseline. Our approach is applicable as a flexible self-supervised projection inpainting tool for tomographic applications.

翻译：计算机断层扫描（CT）的诊断质量通常受限于患者辐射剂量、扫描速度及图像质量。稀疏角度断层扫描虽能降低辐射暴露并加速数据采集，但会引入图像伪影与噪声。现有图像处理算法可恢复CT重建质量，但通常需要大规模训练数据集或无法用于截断物体。本文提出一种自监督投影修补方法，通过基于梯度的优化实现缺失投影视角的优化。通过重建独立的投影数据堆栈，在CT图像域计算自监督损失，并直接优化投影图像强度以匹配受投影几何约束的缺失断层视角。我们在真实X射线显微镜（XRM）断层扫描小鼠胫骨数据上的实验表明，与插值基线相比，本方法在PSNR/SSIM指标上分别提升3.1-7.4%/7.7-17.6%。该方法可作为灵活的自监督投影修补工具应用于断层扫描领域。