Kang Seung Kwan, An Hyun Joon, Jin Hyeongmin, Kim Jung-In, Chie Eui Kyu, Park Jong Min, Lee Jae Sung
Department of Biomedical Sciences and Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 South Korea.
Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, 03080 South Korea.
Biomed Eng Lett. 2021 Jun 19;11(3):263-271. doi: 10.1007/s13534-021-00195-8. eCollection 2021 Aug.
Although MR-guided radiotherapy (MRgRT) is advancing rapidly, generating accurate synthetic CT (sCT) from MRI is still challenging. Previous approaches using deep neural networks require large dataset of precisely co-registered CT and MRI pairs that are difficult to obtain due to respiration and peristalsis. Here, we propose a method to generate sCT based on deep learning training with weakly paired CT and MR images acquired from an MRgRT system using a cycle-consistent GAN (CycleGAN) framework that allows the unpaired image-to-image translation in abdomen and thorax. Data from 90 cancer patients who underwent MRgRT were retrospectively used. CT images of the patients were aligned to the corresponding MR images using deformable registration, and the deformed CT (dCT) and MRI pairs were used for network training and testing. The 2.5D CycleGAN was constructed to generate sCT from the MRI input. To improve the sCT generation performance, a perceptual loss that explores the discrepancy between high-dimensional representations of images extracted from a well-trained classifier was incorporated into the CycleGAN. The CycleGAN with perceptual loss outperformed the U-net in terms of errors and similarities between sCT and dCT, and dose estimation for treatment planning of thorax, and abdomen. The sCT generated using CycleGAN produced virtually identical dose distribution maps and dose-volume histograms compared to dCT. CycleGAN with perceptual loss outperformed U-net in sCT generation when trained with weakly paired dCT-MRI for MRgRT. The proposed method will be useful to increase the treatment accuracy of MR-only or MR-guided adaptive radiotherapy.
The online version contains supplementary material available at 10.1007/s13534-021-00195-8.
尽管磁共振引导放疗（MRgRT）发展迅速，但从磁共振成像（MRI）生成准确的合成计算机断层扫描（sCT）仍然具有挑战性。以前使用深度神经网络的方法需要精确配准的CT和MRI对的大型数据集，由于呼吸和蠕动，这些数据集很难获得。在此，我们提出一种基于深度学习训练生成sCT的方法，该方法使用从MRgRT系统获取的弱配对CT和MR图像，采用循环一致生成对抗网络（CycleGAN）框架，该框架允许在腹部和胸部进行非配对图像到图像的转换。回顾性使用了90例接受MRgRT的癌症患者的数据。使用可变形配准将患者的CT图像与相应的MR图像对齐，变形后的CT（dCT）和MRI对用于网络训练和测试。构建2.5D CycleGAN以从MRI输入生成sCT。为了提高sCT生成性能，将探索从训练良好的分类器提取的图像高维表示之间差异的感知损失纳入CycleGAN。在sCT与dCT之间的误差和相似度、胸部和腹部治疗计划的剂量估计方面，具有感知损失的CycleGAN优于U-net。与dCT相比，使用CycleGAN生成的sCT产生了几乎相同的剂量分布图和剂量体积直方图。当使用弱配对的dCT-MRI进行MRgRT训练时，具有感知损失的CycleGAN在sCT生成方面优于U-net。所提出的方法将有助于提高仅使用磁共振或磁共振引导的自适应放疗的治疗准确性。
在线版本包含可在10.1007/s13534-021-00195-8获取的补充材料。
