Monte Carlo simulation fused with target distribution modeling via deep reinforcement learning for automatic high-efficiency photon distribution estimation-Reference-Cited by-同舟云学术

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

Published:2021-02-08 Issue:3 Volume:9 Page:B45
ISSN:2327-9125
Container-title:Photonics Research
language:en
Short-container-title:Photon. Res.

Author:

Ma Jianhui,Piao Zun,Huang Shuang,Duan Xiaoman,Qin Genggeng,Zhou Linghong,Xu Yuan

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Ministry of Science and Technology Planning Project of Guangdong

Guangzhou Science and technology plan project

Publisher

The Optical Society

Subject

Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference39 articles.

1. Statistical distributions in particle technology;Verheijen,2001

2. Estimating the mean and variance of the target probability distribution;Nix,1994

3. Advances in Image-Guided Radiation Therapy

4. Scatter estimation for a digital radiographic system using convolution filtering

5. Efficient scatter correction using asymmetric kernels

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