Monte Carlo simulation fused with target distribution modeling via deep reinforcement learning for automatic high-efficiency photon distribution estimation

Particle distribution estimation is an important issue in medical diagnosis. In particular, photon scattering in some medical devices extremely degrades image quality and causes measurement inaccuracy. The Monte Carlo (MC) algorithm is regarded as the most accurate partide estimation approach but is still time-consuming, even with graphic processing unit (GPU) acceleration. The goal of this work is to develop an automatic scatter estimation framework for high-efficiency photon distribution estimation. Specifically, a GPU-based MC simulation initially yields a raw scatter signal with a low photon number to hasten scatter generation. In the proposed method, assume that the scatter signal follows Poisson distribution, where an optimization objective function fused with sparse feature penalty is modeled. Then, an over-relaxation algorithm is deduced mathematically to solve this objective function. For optimizing the parameters in the over-relaxation algorithm, the deep Q-network in the deep reinforcement learning scheme is built to intelligently interact with the over-relaxation algorithm to accurately and rapidly estimate a scatter signal with the large range of photon numbers. Experimental results demonstrated that our proposed framework can achieve superior performance with structural similarity >0.94, peak signal-to-noise ratio >26.55 dB, and relative absolute error <5.62%, and the lowest computation time for one scatter image generation can be within 2 s. (C) 2021 Chinese Laser Press

Automated summary

This work aims to develop an automatic scatter estimation framework for efficient photon distribution estimation, utilizing GPU-based MC simulations and an over-relaxation algorithm combined with deep reinforcement learning. The proposed method can rapidly and accurately estimate scatter signals with a large range of photon numbers, achieving superior performance in terms of structural similarity, peak signal-to-noise ratio, and relative absolute error.