Learning CT scatter estimation without labeled data: a feasibility study

Abstract

Since X-ray scattering is a major cause of artifacts, its correction is a crucial step in almost any CT application. Most existing approaches, however, are based on complex theoretical models that need to be tailored to that particular application. To perform scatter estimation in absence of such models, we propose the unsupervised deep scatter estimation (uDSE). Here, uDSE combines a scatter estimation network that operates in projection domain with a scatter correction layer and CT reconstruction layer. In that way scatter estimation can be trained using an unsupervised Wassersten GAN (WGAN) setup in which the parameters of the scatter estimation network are optimized such that the resulting scatter corrected reconstructions cannot be distinguished from samples of a true artifact-free reference set. To demonstrate the feasibility of the proposed approach, uDSE is evaluated for simulated CBCT scans. Applied to the corresponding test data, uDSE is able to remove most of the present scatter artifacts and yields similar CT value accuracy (mean error of 27.9 HU vs. 24.7 HU) as a state-of-the-art supervised scatter estimation approach. Thus, uDSE may be used in the future to learn scatter estimation in cases where labels are not available or cannot be generated with sufficient accuracy.