Impact of a data-derivative fidelity on truncation-data reconstruction in CBCT

In practical CBCT imaging such as that in interventional procedures, data collected are truncated due to the limited detector size that cannot cover the imaged subject and especially the involved interventional devices. We study optimization-based image reconstruction directly from truncated data, with an aim to reduce truncation artifacts observed in practical or clinical reconstructions. In the study, the reconstruction problem is formulated as a constrained optimization program in which a data-derivative fidelity term was introduced for effective suppression of the truncation artifacts. In this work, we carry specifically out a quantitative investigation of impact of the data-derivative term on the artifact reduction. In the investigation, the contribution of the data-derivative term is characterized by a weighting parameter, and the investigation is enabled subsequently by tailoring the Chambolle-Pock (CP) algorithm to solve the optimization program containing the data-derivative fidelity term. The results of the study suggest that an appropriately designed data-derivative fidelity can be exploited for yielding image reconstruction with reduced artifacts directly from truncated data.