Pseudo CT Synthesis Using Cone-Beam CT of Cervical Cancer with GAN-Based Neural Network Model.

Cervical cancer (CC) is a tumor disease that threatens the health of women. As an important treatment of CC, radiotherapy has been widely used in clinic. With the rapid development of radiotherapy technology, adaptive radiotherapy has received much attention. Adaptive radiotherapy means more accurate radiation dose and more accurate radiation area, which can effectively protect normal tissue. It is significant to improve the local control rate of tumor and the quality of life of patients. However, the Cone-Beam CT (CBCT) images collected during radiotherapy are of poor quality and cannot provide real-time radiation effect information, resulting in timely and effective adjustment of radiation dose and radiation area in the process of radiotherapy for cervical cancer. To alleviate this issue, this study will establish a model to leverage CC CBCT images to synthetize pseudo computed tomography (CT) images with high quality, so as to achieve the purpose of quality improvement. This study included the data of 20 patients with CC in ** hospital. The planning CT and CBCT scan data of each patient before radiotherapy were collected, and the interval between the two kinds of image data was required to be less than one week. After data preprocessing, a total of 1206 pairs of images were trained and tested. The generative adversarial network (GAN) is constructed. In order to ensure the similarity between the input image and the output image, the L1 loss function is leveraged. And the full supervision method is used to train the model to achieve a better effect of image synthesis and improve the quality of CBCT image. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were used as evaluation indexes. Using five-fold cross-validation, the values of PSNR between the pseudo-CT (sCT) and the planning CT (pCT) image and between the CBCT and the pCT image are calculated. The results are 26.9 and 22.6, respectively. The sCT obtained from the GAN model increases the peak signal-to-noise ratio by 19% compared with the original CBCT, which means that the proposed model built in this study can improve the useful information of the CBCT image. The SSIM values between sCT and pCT and between CBCT and pCT are also calculated, and the average values of them are 0.89 and 0.63, respectively. Therefore, in this experiment, the structure of the sCT obtained by the proposed model is closer to pCT. And the SSIM increases by 41.2% compared with the original CBCT, which means that the sCT by the proposed model is more similar to the pCT in structure. These results could make a more accurate judgment on the effect of radiotherapy. In this study, the pseudo-CT synthesis method based on GAN can improve the quality of CC CBCT image. The results show this method makes the structure clearer and could assist doctors to adjust the radiation dose and radiation area in time. This study is able to facilitate the development of adaptive radiotherapy for CC. [ABSTRACT FROM AUTHOR]