Improving synthetic CT accuracy by combining the benefits of multiple normalized preprocesses

Abstract

To investigate the effect of different normalization preprocesses in deep learning on the accuracy of different tissues in synthetic computed tomography (sCT) and to combine their advantages to improve the accuracy of all tissues. Methods: The cycle-consistent adversarial network (CycleGAN) model was used to generate sCT images from megavolt cone-beam CT (MVCBCT) images. In this study, 2639 head MVCBCT and CT image pairs from 203 patients were collected as a training set, and 249 image pairs from 29 patients were collected as a test set. We normalized the voxel values in images to 0 to 1 or −1 to 1, using two linear and five nonlinear normalization preprocessing methods to obtain seven data sets and compared the accuracy of different tissues in different sCT obtained from training these data. Finally, to combine the advantages of different normalization preprocessing methods, we obtained sCT_Blur by cropping, stitching, and smoothing (OpenCV's cv2.medianBlur, kernel size 5) each group of sCTs and evaluated its image quality and accuracy of OARs. Results: Different normalization preprocesses made sCT more accurate in different tissues. The proposed sCT_Blur took advantage of multiple normalization preprocessing methods, and all tissues are more accurate than the sCT obtained using a single conventional normalization method. Compared with other sCT images, the structural similarity of sCT_Blur versus CT was improved to 0.906 ± 0.019. The mean absolute errors of the CT numbers were reduced to 15.7

Effect of metal implants and metal artifacts on back‐projected two‐dimensional entrance fluence determined by EPID dosimetry

“…However, our radiotherapy center has only one CBCT scanner that can provide online images. Although we previously developed a method to convert CBCT images into synthetic CT images with reduced metal artifacts, the RED information in the synthetic CT images may not be completely accurate, which may introduce additional errors in the back‐projection calculation 15,19 . In this study, we used a simulated phantom with a rigid structure and no anatomical variations.…”

“…Although we previously developed a method to convert CBCT images into synthetic CT images with reduced metal artifacts, the RED information in the synthetic CT images may not be completely accurate, which may introduce additional errors in the back-projection calculation. 15,19 In this study, we used a simulated phantom with a rigid structure and no anatomical variations. The phantom contained only the head region, which was easy to set up, and the setup error in all directions were less than 1 mm.…”