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MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning

BACKGROUND: Radical radiotherapy is the main treatment modality for early and locally advanced nasopharyngeal carcinoma (NPC). Magnetic resonance imaging (MRI) has the advantages of no ionizing radiation and high soft-tissue resolution compared to computed tomography (CT), but it does not provide el...

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Autores principales: Ma, Xiangyu, Chen, Xinyuan, Li, Jingwen, Wang, Yu, Men, Kuo, Dai, Jianrong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457879/
https://www.ncbi.nlm.nih.gov/pubmed/34568044
http://dx.doi.org/10.3389/fonc.2021.713617
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author Ma, Xiangyu
Chen, Xinyuan
Li, Jingwen
Wang, Yu
Men, Kuo
Dai, Jianrong
author_facet Ma, Xiangyu
Chen, Xinyuan
Li, Jingwen
Wang, Yu
Men, Kuo
Dai, Jianrong
author_sort Ma, Xiangyu
collection PubMed
description BACKGROUND: Radical radiotherapy is the main treatment modality for early and locally advanced nasopharyngeal carcinoma (NPC). Magnetic resonance imaging (MRI) has the advantages of no ionizing radiation and high soft-tissue resolution compared to computed tomography (CT), but it does not provide electron density (ED) information for radiotherapy planning. Therefore, in this study, we developed a pseudo-CT (pCT) generation method to provide necessary ED information for MRI-only planning in NPC radiotherapy. METHODS: Twenty patients with early-stage NPC who received radiotherapy in our hospital were investigated. First, 1433 sets of paired T1 weighted magnetic resonance (MR) simulation images and CT simulation images were rigidly registered and preprocessed. A 16-layer U-Net was used to train the pCT generative model and a “pix2pix” generative adversarial network (GAN) was also trained to compare with the pure U-Net regrading pCT quality. Second, the contours of all target volumes and organs at risk in the original CT were transferred to the pCT for planning, and the beams were copied back to the original CT for reference dose calculation. Finally, the dose distribution calculated on the pCT was compared with the reference dose distribution through gamma analysis and dose-volume indices. RESULTS: The average time for pCT generation for each patient was 7.90 ± 0.47 seconds. The average mean (absolute) error was −9.3 ± 16.9 HU (102.6 ± 11.4 HU), and the mean-root-square error was 209.8 ± 22.6 HU. There was no significant difference between the pCT quality of pix2pix GAN and that of pure U-Net (p > 0.05). The dose distribution on the pCT was highly consistent with that on the original CT. The mean gamma pass rate (2 mm/3%, 10% low dose threshold) was 99.1% ± 0.3%, and the mean absolute difference of nasopharyngeal PGTV D(99%) and PTV V(95%) were 0.4% ± 0.2% and 0.1% ± 0.1%. CONCLUSION: The proposed deep learning model can accurately predict CT from MRI, and the generated pCT can be employed in precise dose calculations. It is of great significance to realize MRI-only planning in NPC radiotherapy, which can improve structure delineation and considerably reduce additional imaging dose, especially when an MR-guided linear accelerator is adopted for treatment.
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spelling pubmed-84578792021-09-23 MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning Ma, Xiangyu Chen, Xinyuan Li, Jingwen Wang, Yu Men, Kuo Dai, Jianrong Front Oncol Oncology BACKGROUND: Radical radiotherapy is the main treatment modality for early and locally advanced nasopharyngeal carcinoma (NPC). Magnetic resonance imaging (MRI) has the advantages of no ionizing radiation and high soft-tissue resolution compared to computed tomography (CT), but it does not provide electron density (ED) information for radiotherapy planning. Therefore, in this study, we developed a pseudo-CT (pCT) generation method to provide necessary ED information for MRI-only planning in NPC radiotherapy. METHODS: Twenty patients with early-stage NPC who received radiotherapy in our hospital were investigated. First, 1433 sets of paired T1 weighted magnetic resonance (MR) simulation images and CT simulation images were rigidly registered and preprocessed. A 16-layer U-Net was used to train the pCT generative model and a “pix2pix” generative adversarial network (GAN) was also trained to compare with the pure U-Net regrading pCT quality. Second, the contours of all target volumes and organs at risk in the original CT were transferred to the pCT for planning, and the beams were copied back to the original CT for reference dose calculation. Finally, the dose distribution calculated on the pCT was compared with the reference dose distribution through gamma analysis and dose-volume indices. RESULTS: The average time for pCT generation for each patient was 7.90 ± 0.47 seconds. The average mean (absolute) error was −9.3 ± 16.9 HU (102.6 ± 11.4 HU), and the mean-root-square error was 209.8 ± 22.6 HU. There was no significant difference between the pCT quality of pix2pix GAN and that of pure U-Net (p > 0.05). The dose distribution on the pCT was highly consistent with that on the original CT. The mean gamma pass rate (2 mm/3%, 10% low dose threshold) was 99.1% ± 0.3%, and the mean absolute difference of nasopharyngeal PGTV D(99%) and PTV V(95%) were 0.4% ± 0.2% and 0.1% ± 0.1%. CONCLUSION: The proposed deep learning model can accurately predict CT from MRI, and the generated pCT can be employed in precise dose calculations. It is of great significance to realize MRI-only planning in NPC radiotherapy, which can improve structure delineation and considerably reduce additional imaging dose, especially when an MR-guided linear accelerator is adopted for treatment. Frontiers Media S.A. 2021-09-08 /pmc/articles/PMC8457879/ /pubmed/34568044 http://dx.doi.org/10.3389/fonc.2021.713617 Text en Copyright © 2021 Ma, Chen, Li, Wang, Men and Dai https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Ma, Xiangyu
Chen, Xinyuan
Li, Jingwen
Wang, Yu
Men, Kuo
Dai, Jianrong
MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning
title MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning
title_full MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning
title_fullStr MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning
title_full_unstemmed MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning
title_short MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning
title_sort mri-only radiotherapy planning for nasopharyngeal carcinoma using deep learning
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457879/
https://www.ncbi.nlm.nih.gov/pubmed/34568044
http://dx.doi.org/10.3389/fonc.2021.713617
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