Cardiac Substructure Delineation Based on Synthetic Contrast-Enhanced CT Generation Using Deep Convolutional Neural Network in Breast Cancer Radiation Therapy

Abstract

Although radiation-induced cardiac toxicity is an important issue in breast radiation therapy (RT), the dose relationship between cardiac structures and its toxicity has not been fully elucidated, partially because many centers do not routinely administer intravenous (IV) contrast for breast RT, which preclude substructure delineation and detection of potential correlations. In this study, we attempted to generate the synthetic contrast-enhanced CT (CECTsyn) from the non-contrast CT (NCTreal) using deep convolutional neural network (DCNN) and to investigate whether CECTsyn can take a supportive role in case the studies for the cardiac toxicity induced by radiation is needed for the patient whose CECTreal cannot be obtained.For this study, 22 NCTreal-CECTreal cardiac scan-pairs have been prepared of which the volume size was ∼512 × 512 × 400 and the resolution was ∼0.75 × 0.75 × 1 mm3 on average. Of the 22 datasets, 13/2/7 were used for training, validation, and testing, respectively. After matching the structure of paired scans using the deformable image registration (DIR), the area near the heart was cropped and used for training the deep learning model. We adopted the modified 2D fully convolution DenseNet (FC-DenseNet) as our backbone and trained it in the conditional generative adversarial network (cGAN) framework. The similarity between CECTsyn and CECTreal was evaluated first, and all NCTreal,CECTsyn, and CECTreal were applied to pre-trained cardiac auto-segmentation models to obtain the substructures of the heart, which is used for dose assessments in each of them. Finally, dose evaluations were conducted on CECTsyn and CECTreal with the dose distributions which are from clinical treatment plans and the manually modified contours of cardiac substructures.Firstly, the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) between CECTsyn and CECTreal were 23.96 and 0.772, whereas those of NCTreal and CECTreal were 22.25and 0.748, respectively. Secondly, the results of applying the pre-trained cardiac auto-segmentation model were obtained, and the dice similarity coefficient (DSC) of CECTsyn was 0.762 on average which is superior to 0.267 of NCTreal and comparable to 0.843 of CECTreal. We could distinguish the L/R ventricles, L/R atriums, left anterior descending artery (LAD), and right coronary artery (RCA) clearly on CECTsyn. Finally, the dose differences between CECTsyn and CECTreal were on average 0.4/2.0 Gy in Dmean and Dmax, and 2.1/1.3/0.6/0.4/0.3/0.1/0.0% in V5 Gy, V10 Gy, V20 Gy, V30 Gy, V40 Gy, V50 Gy, and V60 Gy, respectively.DCNN model proposed here showed the feasibility of CECTsyn generation from NCTreal and potential for cardiac substructure delineation such as ventricles, atriums, LAD, and RCA on NCTreal for breast RT. Furthermore, it is shown that CECTsyn can play a supportive role when the studies for the radiation-induced cardiac toxicity is needed.