Photonics Research, Volume. 9, Issue 3, B45(2021)
Monte Carlo simulation fused with target distribution modeling via deep reinforcement learning for automatic high-efficiency photon distribution estimation
Fig. 1. Automatic scatter estimation framework. The MC algorithm generates raw scatter signals in terms of the X-ray source energy spectrum and system geometry configuration. The DRL scheme (denoted by the dashed black arrow) employs a deep
Fig. 2. Network architecture in the DDQN. The network takes a scatter image as input and predicts three possible actions for parameter adjustment. The number at the top denotes the feature map size and channel number, and the operations for each layer are presented at the bottom. For instance, the first hidden layer convolves 16 filters of
Fig. 3. (a) is the primary projection of the head and neck (H&N) patient; (b)–(i) represent raw scatter projections that are separately calculated by the MC particle sampling algorithm with source photons of
Fig. 4. (a)–(g) are the scatter images of Figs.
Fig. 5. Intensity profiles of Fig.
Fig. 6. From top to bottom: six testing results with
Fig. 7. (a)–(d) Intensity profiles of the first, second, third, and last rows in Fig.
Fig. 8. (a)–(c) indicate boxplots of the metric difference of SSIM, PSNR, and RAE between
Fig. 9. Automatic scatter estimation process for a testing case. (a)–(c) are smoothed scatter images at Steps 1, 7, and 13, respectively. (d) and (e) separately plot the SSIM and RAE over steps.
Fig. 10. Different scatter images. From left to right: scatter projection input, the ground truth of the scatter image at the first column, and Grad-CAM heatmaps of three subnetworks
Fig. 11. From top to bottom: four prostate cases with
Fig. 12. (a)–(d) Intensity profiles of the four prostate cases presented in Fig.
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Jianhui Ma, Zun Piao, Shuang Huang, Xiaoman Duan, Genggeng Qin, Linghong Zhou, Yuan Xu. Monte Carlo simulation fused with target distribution modeling via deep reinforcement learning for automatic high-efficiency photon distribution estimation[J]. Photonics Research, 2021, 9(3): B45
Special Issue: DEEP LEARNING IN PHOTONICS
Received: Oct. 26, 2020
Accepted: Dec. 21, 2020
Published Online: Feb. 24, 2021
The Author Email: Linghong Zhou (smart@smu.edu.cn), Yuan Xu (yuanxu@smu.edu.cn)