A correlated sampling‐based Monte Carlo simulation for fast CBCT iterative scatter correction

Abstract

Background: In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging. However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. Purpose: In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. Methods: Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. Results: Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half -fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2 HU in full-fan case and from 53 to 13 HU in half -fan case. Scatterto-primary ratio is reduced to 0.02 for full-fan and 0.04 for half -fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half -fan and full-fan, respectively. The contrast after correction is increased by 19% for half -fan and 44% for fullfan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4 HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling. The proposed method takes less than 25 s for the whole iterative scatter correction process. Conclusions:The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT, making it suitable for real-time clinical use.

Virtual dosimetry study with three cone-beam breast computed tomography scanners using a fast GPU-based Monte Carlo code

“…The comparison of the hardware/software architecture of this work is also of interest with respect to Ghammraoui and Badal (2014), who report 2.9 × 10 8 x-rays/s (i.e., 3.4 s for 10 9 photons) processed by their MC-GPU code running in parallel on a cluster of 8 NVIDIA GTX580 cards. gCTD is also more computationally efficient (by a factor 10 2 ) with respect to the gMCDRR code of Qin et al (2022) written for cone-beam CT scatter correction, which run on the same hardware GPU platform. Using a single NVIDIA Quadro P5000 GPU card and the ImpactMC commercial MC dose simulation software, Shim et al (2023) report a computation speed equal to 1.1-2.6 × 10 7 primary photons s -1 for their simulations on patient-derived anthropomorphic breast phantoms, which is a performance two orders of magnitude worse than in our study.…”