Nullspace-constrained modifications of under-sampled interventional CT images using instrument-specific prior information

Abstract

The use of C-Arm-based cone-beam computed tomography (CBCT) plays an increasing role in interventions, especially for guidance and therapy control. The slow speed and the high dose limit the use of CBCT to research and prevent its widespread application in clinical routine. Acquiring less data using greater angular step or limited angular range is an obvious way to overcome these issues. However, images reconstructed from such datasets using standard reconstruction algorithms are deteriorated with severe artifacts. In this work, we investigate the use of a new nullspace-constrained modification scheme for sparse-view and limited-angle intra-operative CT image reconstruction. This scheme allows to perform fast unconstrained ART reconstruction, and, based on prior knowledge regarding the object to be reconstructed, some modifications restricted to the nullspace of the system can be easily applied as a post-processing step. Within this scheme, we enforce sparsity by integrating geometric prior information regarding the interventional tool itself, besides a high-quality pre-operative CT image. The presented method was compared to the compressed sensing-based algorithms NIHT and PrIDICT. Performance was evaluated qualitatively and quantitatively. This new scheme is shown to be promising for low-dose intra-operative image reconstruction. Compared to PrIDICT and NIHT, it shows higher reconstruction accuracy and demonstrates the ability to precisely visualize the instrument’s position even when only 15 projection views are acquired over a full angular range. It demonstrates an accurate reconstruction with a high degree of robustness against data incompleteness and sparsity level over-estimation.