Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma

Abstract

Background: Although magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis studies based on deep learning have significantly progressed, the similarity between synthetic CT (sCT) and real CT (rCT) has only been evaluated in image quality metrics (IQMs). To evaluate the similarity between synthetic CT (sCT) and real CT (rCT) comprehensively, we comprehensively evaluated IQMs and radiomic features for the first time. Methods: This study enrolled 127 patients with nasopharyngeal carcinoma who underwent CT and MRI scans. Supervised-learning (Unet) and unsupervised-learning (CycleGAN) methods were applied to build MRI-to-CT synthesis models. The regions of interest (ROIs) included nasopharynx gross tumor volume (GTVnx), brainstem, parotid glands, and temporal lobes. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), root mean square error (RMSE), and structural similarity (SSIM) were used to evaluate image quality. Additionally, 837 radiomic features were extracted for each ROI, and the correlation was evaluated using the concordance correlation coefficient (CCC). Results: The MAE, RMSE, SSIM, and PSNR of the body were 91.99, 187.12, 0.97, and 51.15 for Unet and 108.30, 211.63, 0.96, and 49.84 for CycleGAN. For the metrics, Unet was superior to CycleGAN (p < 0.05). For the radiomic features, the percentage of four levels (ie, excellent, good, moderate, and poor, respectively) were as follows: GTVnx, 8.5%, 14.6%, 26.5%, and 50.4% for Unet and 12.3%, 25%, 38.4%, and 24.4% for CycleGAN; other ROIs, 5.44%± 3.27%, 5.56% ± 2.92%, 21.38% ± 6.91%, and 67.58% ± 8.96% for Unet and 5.16% ± 1.69%, 3.5% ± 1.52%, 12.68% ± 7.51%, and 78.62% ± 8.57% for CycleGAN. Conclusions: Unet-sCT was superior to CycleGAN-sCT for the IQMs. However, neither exhibited absolute superiority in radiomic features, and both were far less similar to rCT. Therefore, further work is required to improve the radiomic similarity for MRI-to-CT synthesis. Trial registration: This study was a retrospective study, so it was free from registration.

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images

“…However, because of the often insufficient CT image contrast for soft tissues, several studies recommended the integration of complementary magnetic resonance (MR) modality 4,33 . This is important especially from the perspective of OAR contouring, 4,34,35 synthetic MR image generation for MR‐aided RT, 36,37 synthetic CT image generation for MR‐only RT 38–41 and MR‐guided RT 42,43 . While some OARs can be accurately and reliably contoured in CT images (i.e., bone structures such as, e.g., the mandible), MR images are often used to better visualize soft tissues.…”

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

“…Dai et al [16] used CycleGAN to generate synthetic CT from CBCT of breast cancer patients, and then input the 3D U-Net segmentation network trained by PCT, so as to achieve CTV segmentation on CBCT images of breast cancer. However, Yuan et al [17] pointed out that the similarity between synthetic images and PCT in radiomics features was quite different, and some error information may be synthesized, which still needs further study. Most of the existing deep learning-based segmentation methods rely on the training of a large number of labeled data.…”