Portability of TV-Regularized Reconstruction Parameters to Varying Data Sets

Abstract

In C-arm computed tomography there are certain constraints due to the data acquisition process which can cause limited raw data. The reconstructed image’s quality may significantly decrease depending on these constraints. To compensate for severely under-sampled projection data during reconstruction, special algorithms have to be utilized, more robust to such ill-posed problems. In the past few years it has been shown that reconstruction algorithms based on the theory of compressed sensing are able to handle incomplete data sets quite well. In this paper, the iterative iTV reconstruction method by Ludwig Ritschl et al. is analyzed regarding it’s elimination capabilities of image artifacts caused by incomplete raw data with respect to the settings of it’s various parameters. The evaluation of iTV and the data dependency of iterative reconstruction’s parameters is conducted in two stages. First, projection data with severe angular under-sampling is acquired using an analytical phantom. Proper reconstruction parameters are selected by analyzing the reconstruction results from a set of proposed parameters. In a second step multiple phantom data sets are acquired with limited angle geometry and a small number of projections. The iTV reconstructions of these data sets are compared to short-scan FDK and SART reconstruction results, highlighting the distinct data dependence of the iTV reconstruction parameters.