Scatter correction based on adaptive photon path-based Monte Carlo simulation method in Multi-GPU platform

Yangmei Zhang; Yusi Chen; Anni Zhong; Xun Jia; Shuyu Wu; Hongliang Qi; Linghong Zhou; Yuan Xu

doi:10.1016/j.cmpb.2020.105487

Scatter correction based on adaptive photon path-based Monte Carlo simulation method in Multi-GPU platform

Comput Methods Programs Biomed. 2020 Oct:194:105487. doi: 10.1016/j.cmpb.2020.105487. Epub 2020 May 11.

Authors

Yangmei Zhang¹, Yusi Chen¹, Anni Zhong¹, Xun Jia², Shuyu Wu³, Hongliang Qi³, Linghong Zhou⁴, Yuan Xu⁵

Affiliations

¹ School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 510515.
² Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA.
³ Guangzhou Huaduan Technology Limited Company, Guangzhou, China, 510700.
⁴ School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 510515. Electronic address: smart@smu.edu.cn.
⁵ School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 510515. Electronic address: yuanxu@smu.edu.cn.

PMID: 32473514
DOI: 10.1016/j.cmpb.2020.105487

Abstract

Monte Carlo (MC)-based simulation is the most precise method in scatter correction for Cone-beam CT (CBCT). Nonetheless, the existing MC methods cannot be fully applied in clinical due to its low efficiency. The traditional MC simulations perform calculations via a particle-by-particle scheme, which leads to high computation costs because abundant photons do not reach the X-ray detector in transport. The conventional approaches cannot control where the particle ends. Hence, it unavoidably waste lots of time in transporting numerous photons that have no contribution to the signal at the detector, yielding a low computational efficiency. To solve the problem, an innovative GPU-based Metropolis MC (gMMC) method was proposed. Compared with the traditional ones, the Metropolis based algorithm utilizes a path-by-path sampling method. The method can automatically control each particle path and eventually accelerate the convergence. In this paper, we firstly take planning CT image as prior information because of its precise CT value, and utilize gMMC to estimate scatter signal. Then the scatter signals are removed from the raw CBCT projections. Afterwards, FDK reconstruction is performed to obtain the corrected image,some accelerating strategies including reducing photon history number, pixels sampling, projection angles sampling and reconstructed image down-sampling achieve adaptive fast CBCT image reconstruction. For having high computational efficiency, we implemented the whole workflow on a 4-GPU workstation. In order to verify the feasibility of the the method, the experiment of several cases are conducted including simulation, phantom, and real patient cases. Results indicate that the image contrast becomes better, the scatter artifacts are eliminated. The maximum error (e_max), the minimum error (e_min), the 95th percentile error (e_95%), average error (¯e) are reduced from 264, 56, 14 and 21 HU to 28, 10, 3 and 7 HU in full-fan case, and from 387, 5, 19 and 95 HU to 39, 2, 2 and 6 HU in the half-fan case. In terms of computation time, the MC simulation time of all cases is within 2.5 seconds, and the total time is within 15 seconds.

Keywords: Cone-beam CT; Fast Monte Carlo; GPU; Image reconstruction; Scatter correction.

MeSH terms

Algorithms
Cone-Beam Computed Tomography
Humans
Image Processing, Computer-Assisted*
Monte Carlo Method
Phantoms, Imaging
Photons*
Scattering, Radiation