Implementation of an efficient monte carlo calculation for CBCT scatter correction: Phantom study

Abstract

Cone-beam computed tomography (CBCT) images suffer from poor image quality, in a large part due to contamination from scattered X-rays. In this work, a Monte Carlo (MC)-based iterative scatter correction algorithm was implemented on measured phantom data acquired from a clinical on-board CBCT scanner. An efficient EGSnrc user code (egs_cbct) was used to transport photons through an uncorrected CBCT scan of a Catphan 600 phantom. From the simulation output, the contribution from primary and scattered photons was estimated in each projection image. From these estimates, an iterative scatter correction was performed on the raw CBCT projection data. The results of the scatter correction were compared with the default vendor reconstruction. The scatter correction was found to reduce the error in CT number for selected regions of interest, while improving contrast-to-noise ratio (CNR) by 18%. These results demonstrate the performance of the proposed scatter correction algorithm in improving image quality for clinical CBCT images.