Open AccessDosimetric evaluation of synthetic CT image generated using a neural network for MR‐only brain radiotherapy
Author(s) -
Tang Bin,
Wu Fan,
Fu Yuchuan,
Wang Xianliang,
Wang Pei,
Orlandini Lucia Clara,
Li Jie,
Hou Qing
Publication year - 2021
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.13176
Subject(s) - computer science , discriminator , nuclear medicine , magnetic resonance imaging , artificial neural network , artificial intelligence , workflow , radiation therapy , medicine , pattern recognition (psychology) , radiology , telecommunications , database , detector
Purpose and background The magnetic resonance (MR)‐only radiotherapy workflow is urged by the increasing use of MR image for the identification and delineation of tumors, while a fast generation of synthetic computer tomography (sCT) image from MR image for dose calculation remains one of the key challenges to the workflow. This study aimed to develop a neural network to generate the sCT in brain site and evaluate the dosimetry accuracy. Materials and methods A generative adversarial network (GAN) was developed to translate T1‐weighted MRI to sCT. First, the "U‐net" shaped encoder‐decoder network with some image translation‐specific modifications was trained to generate sCT, then the discriminator network was adversarially trained to distinguish between synthetic and real CT images. We enrolled 37 brain cancer patients acquiring both CT and MRI for treatment position simulation. Twenty‐seven pairs of 2D T1‐weighted MR images and rigidly registered CT image were used to train the GAN model, and the remaining 10 pairs were used to evaluate the model performance through the metric of mean absolute error. Furthermore, the clinical Volume Modulated Arc Therapy plan was calculated on both sCT and real CT, followed by gamma analysis and comparison of dose‐volume histogram. Results On average, only 15 s were needed to generate one sCT from one T1‐weighted MRI. The mean absolute error between synthetic and real CT was 60.52 ± 13.32 Housefield Unit over 5‐fold cross validation. For dose distribution on sCT and CT, the average pass rates of gamma analysis using the 3%/3 mm and 2%/2 mm criteria were 99.76% and 97.25% over testing patients, respectively. For parameters of dose‐volume histogram for both target and organs at risk, no significant differences were found between both plans. Conclusion The GAN model can generate synthetic CT from one single MRI sequence within seconds, and a state‐of‐art accuracy of CT number and dosimetry was achieved.