Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy

Liugang Gao,Chunying Li,Jiawei Sun,Jianfeng Sui,Xiaojin Wu,Kai Xie,Tao Lin,Zhengda Lu,Xinye Ni

doi:10.1186/s13014-021-01928-w

Liugang Gao, Chunying Li + Show 7 more

Open Access

https://doi.org/10.1186/s13014-021-01928-w

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Abstract

ObjectiveTo develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.MethodsThe CBCT/planning CT images of 170 patients undergoing thoracic radiotherapy were used for training and testing. The CBCT images were scanned under a fast protocol with 50% less clinical projection frames compared with standard chest M20 protocol. Training with aligned paired images was performed using conditional adversarial networks (so-called pix2pix), and training with unpaired images was carried out with cycle-consistent adversarial networks (cycleGAN) and AGGAN, through which sCT images were generated. The image quality and Hounsfield unit (HU) value of the sCT images generated by the three neural networks were compared. The treatment plan was designed on CT and copied to sCT images to calculated dose distribution.ResultsThe image quality of sCT images by all the three methods are significantly improved compared with original CBCT images. The AGGAN achieves the best image quality in the testing patients with the smallest mean absolute error (MAE, 43.5 ± 6.69), largest structural similarity (SSIM, 93.7 ± 3.88) and peak signal-to-noise ratio (PSNR, 29.5 ± 2.36). The sCT images generated by all the three methods showed superior dose calculation accuracy with higher gamma passing rates compared with original CBCT image. The AGGAN offered the highest gamma passing rates (91.4 ± 3.26) under the strictest criteria of 1 mm/1% compared with other methods. In the phantom study, the sCT images generated by AGGAN demonstrated the best image quality and the highest dose calculation accuracy.ConclusionsHigh-quality sCT images were generated from low-dose thoracic CBCT images by using the proposed AGGAN through unpaired CBCT and CT images. The dose distribution could be calculated accurately based on sCT images in radiotherapy.

Highlights

Cone-beam CT (CBCT) images are widely used in imageguided radiotherapy (IGRT) [1,2,3], and they are important for decreasing the positioning error and increasing the accuracy of treatments for patients with cancer
The columns from left to right display CBCT, CT, and synthetic CT (sCT) images generated by pix2pix, cycleGAN, and attention-guided generative adversarial networks (AGGAN), respectively
Most of the artifacts in the sCT images generated by pix2pix were eliminated, but several anatomical structures, especially the bone, cavity, and lung marking regions, in the images were destroyed

Summary

Introduction

Cone-beam CT (CBCT) images are widely used in imageguided radiotherapy (IGRT) [1,2,3], and they are important for decreasing the positioning error and increasing the accuracy of treatments for patients with cancer. Compared with images from traditional fan-beam CT, CBCT images suffer from low contrast and artifacts due. CBCT images are unsuitable for calculating dose distributions for replanning in adaptive radiotherapy. Patients may undergo multiple CBCT scans during an IGRT treatment course and this raises a great concern about delivered dose to the patients. To reduce the additional dose for patients generated from IGRT, the researchers have proposed several low-dose CBCT imaging technologies [7, 8]. The low-dose protocols of CBCT scanning have been widely used in clinical practice

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