Abstract
Purpose: To develop a fast Monte‐Carlo‐based scatter‐correction algorithm for clinical keV cone‐beam CT (CBCT) images. Method and Materials: Estimates of the scatter in the projection‐views of a CBCT scan were obtained by an iterative process, each step consisting of: (1) a coarse CBCT reconstruction; (2) simulation of photon histories for projections using a purpose‐written Monte Carlo code; (3) scoring scatter contributions to fixed points on the detector (a “forced detection” technique); and (4) subtraction of scatter‐estimates from the measured pixel‐values. The scatter signal at each pixel was estimated using linear interpolation spatially between the values calculated at the fixed points and angularly between projection angles. Following convergence to a set of scatter‐corrected profiles, a final full‐resolution scatter‐corrected reconstruction was performed. All CBCT reconstructions were performed using software developed in‐house. The x‐ray tube spectrum and the energy‐response of the detector were both modeled. To validate the technique, projection measurements (120 kV and 0.4 mAs per projection) of a Catphan quality‐assurance phantom (The Phantom Laboratory) were obtained using a Synergy XVI CBCT unit (Elekta Limited). Results: Typically the algorithm took less than 2 min to complete 4 iterations on a desktop PC, after which convergence was obtained. Qualitatively, the algorithm resulted in an improved image with the characteristic ‘cupping’ artifacts, due to scatter, disappearing. Quantitatively, non‐uniformity was decreased after correction from about 15% to 1% or less at a cost of an increase in image noise from 3.7% to 5.1%. CT number accuracy was also markedly improved. Conclusion: It was shown Monte‐Carlo‐based scatter‐correction of clinical keV CBCT images does not have to be prohibitively slow. Such a scatter‐correction can be successfully performed in a few CPU minutes.