Boron concentration prediction from Compton camera image for boron neutron capture therapy based on generative adversarial network

Zhenfeng Hou; Changran Geng; Xiaobin Tang; Feng Tian; Sheng Zhao; Jie Qi; Diyun Shu; Chunhui Gong

doi:10.1016/j.apradiso.2022.110302

Boron concentration prediction from Compton camera image for boron neutron capture therapy based on generative adversarial network

Appl Radiat Isot. 2022 Aug:186:110302. doi: 10.1016/j.apradiso.2022.110302. Epub 2022 May 25.

Authors

Zhenfeng Hou¹, Changran Geng², Xiaobin Tang³, Feng Tian¹, Sheng Zhao¹, Jie Qi¹, Diyun Shu¹, Chunhui Gong⁴

Affiliations

¹ Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
² Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Joint International Research Laboratory on Advanced Particle Therapy, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Key Laboratory of Nuclear Technology Application and Radiation Protection in Astronautics, Ministry of Industry and Information Technology, Nanjing, 210016, China. Electronic address: gengchr@nuaa.edu.cn.
³ Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Joint International Research Laboratory on Advanced Particle Therapy, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Key Laboratory of Nuclear Technology Application and Radiation Protection in Astronautics, Ministry of Industry and Information Technology, Nanjing, 210016, China.
⁴ School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

PMID: 35653926
DOI: 10.1016/j.apradiso.2022.110302

Abstract

Prompt gamma monitoring for the prediction of boron concentration is valuable for the dose calculation of boron neutron capture therapy (BNCT). This work proposes to use generative adversarial network (GAN) to predict the boron distribution based on Compton camera (CC) imaging quickly and provide a scientific basis for its application in BNCT. The BNCT and Compton imaging process was simulated, then the image reconstructed from the simulation and the contour of skin from CT are used as input, and the distribution of boron concentration from PET data is set as the output to train the network. The structural similarity, peak signal-to-noise ratio, and root mean square error of the images generated by the trained network are improved significantly, and the ratio of the boron concentration between the tumor area and the normal tissue is improved from 1.55 to 3.85, which is much closer to the true value of 3.52. The trained network can optimize the original image within 0.83 s, which is much faster than iterative optimization. The proposed method could help to ease the current online monitoring problem of boron concentration on a computational level, thereby promoting the clinical development of BNCT technology.

Keywords: BNCT; Boron imaging; Compton camera; Generative adversarial network.

MeSH terms

Boron / therapeutic use
Boron Neutron Capture Therapy* / methods
Computer Simulation
Gamma Rays

Substances

Boron