Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy

Abstract

Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the CycleGAN, Pix2pix and U-Net models were used to generate the sCT images. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were used to quantify the accuracy of the proposed models in a testing cohort of 34 patients. Radiation dose were calculated on pCT and sCT following the same protocol. Dose distributions were evaluated for 4 patients by comparing the dose-volume-histogram (DVH) and 2D gamma index analysis. Results: The average MAE and RMSE values between sCT by three models and pCT reduced by 15.4 HU and 26.8 HU at least, while the mean PSNR and SSIM metrics between sCT by different models and pCT added by 10.6 and 0.05 at most, respectively. There were only slight differences for DVH of selected contours between different plans. The passing rates of 2D gamma index analysis under 3 mm/3% 3 mm/2%, 2 mm/3%and 2 mm/2% criteria were all higher than 95%. Conclusions: All the sCT had achieved better evaluation metrics than those of original CBCT, while the performance of CycleGAN model was proved to be best among three methods. The dosimetric agreement confirmed the HU accuracy and consistent anatomical structures of sCT by deep learning methods.

Keywords: CBCT; deep learning; nasopharyngeal carcinoma; synthetic CT.

Figure 1.

The structures of (a) generator and (b) discriminator used in Pix2pix and CycleGAN models; (c) the total architecture of CycleGAN model, including four generators and two discriminators, respectively.

Figure 2.

The transverse, sagittal and coronal visualization of (a) original CBCT, (b) pCT, (c) sCT by CycleGAN, (d) sCT by Pix2pix and (e) sCT by U-Net models from one selected NPC patient. Display window is [−160, 240] HU.

Figure 3.

Difference map between sCT images generated by different models and pCT. Display window [−160, 240] HU

Figure 4.

Comparison of HU profiles (the second row) of the pink lines on different images as shown in the first row. Display window is [−160, 240] HU.

Figure 5.

Dose distributions comparison of pCT and synthetic CT based treatment plans.

Figure 6.

Comparison of the DVH between pCT and sCT-CycleGAN based plans in one NPC patient. The circled lines, squared lines, triangle lines and diamond lines represent the DVH of plan based on pCT, sCT-CycleGAN, sCT-Pix2pix, and sCT-U-Net, respectively.

Figure 7.

Visualizations of neck regions on (a) CBCT, (b) pCT, (c) sCT-CycleGAN, (d) sCT-Pix2pix and (e) sCT-U-Net. The red bounding boxes represented minimum bounding rectangle of original CBCT body contour. The yellow bounding box showed the same region of muscle on different images.