Dosimetric evaluation of synthetic CT image generated using a neural network for MR‐only brain radiotherapy

Abstract

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 T1weighted 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 T1weighted MRI. The mean absolute error between synthetic and real CT was 60.52 AE 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.

Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network

“…Table III shows near minimum, near maximum, and average dose difference from CT based plan received by the planning target volume (PTV). The difference in average dose to the PTV relative to the prescribed dose was found to be 0.166 ± 0.18% which lies within a clinically acceptable range of dose difference of < 0.5% and comparable with multiple studies in literature [11], [12], [34], [35] IV. DISCUSSION…”

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

“…HU for the whole brain (Figure 7). The PSNR values were above 24 dB for all brain studies [29,30,32,57,62,72,80,97,98,106] (Figure 8). The DSCs were above 0.96 for the body, 0.…”

“…Twenty studies used a cGAN architecture to generate sCT from MRI [31,33,50,[56][57][58][59]89,90,[93][94][95][96][97][98][99][100][101][102].…”

“…Twelve studies used a Pix2Pix architecture [31,50,56,89,90,[93][94][95]98,99,102,104]. Most of these…”

Clinical rationale for in vivo portal dosimetry in magnetic resonance guided online adaptive radiotherapy

“…Deep learning-based synthetic-CT images generated from MRI data [13] , [14] , [15] , [16] could be used in combination with independent dose calculation algorithms for online ‘pre-treatment’ dosimetric verification of the adapted plans. Linac treatment log files, also combined with independent dose calculation algorithms and intrafraction MR imaging, have been used to estimate the daily delivered dose in prostate cancer treatments [17] , [18] .…”