Informed Sinogram Interpolation for Sparse View Reconstruction

Abstract

Computed tomography (CT) has been widely used in medical examinations and non-destructive testing. Micro-CT (microfocus X-ray CT system) is an advanced version that can observe the internal structures of small objects. However, secondary radiation can prevent micro-CT from imaging the full structure of the object. Given the same amount of secondary scans as primary scans, simple subtraction solves the problem at the price of doubled acquisition time. To reduce the acquisition time, one aims to limit the secondary scans to as few imaging angles as possible and interpolate to recover the missing data. Different interpolation methods have been explored for limited-angle tomography, such as polynomial or spline interpolation, compressive sensing, deep learning, etc. In contrast to many of these scenarios, the secondary imaging setup considered in this paper often exhibits simple sinusoidal structures that are not exploited directly by any of the aforementioned approaches. This paper aims to fill this gap with a continuous mathematical model describing the secondary sinogram only with information from the projection space. Building on this model, we propose an interpolation method that effectively interpolates secondary scans from limited observations.