Improved GAN: Using a transformer module generator approach for material decomposition.

Dual-energy computed tomography (CT) can be used for material decomposition, allowing for the precise quantitative mapping of body substances; this has a wide range of clinical applications, including disease diagnosis, treatment response evaluation and prognosis prediction. However, dual-energy CT has not yet become the mainstream technique in most clinical settings due to its limited accessibility. To fully take advantage of material quantification, researchers have attempted to use deep learning to generate material decomposition maps from conventional single-energy CT images, mainly by synthesizing another single-energy CT image from a conventional single-energy CT image to form a dual-energy CT image first and then generate material decomposition maps. This is not a straightforward process, and it potentially introduces many inaccuracies after multiple steps. In this work, we proposed a generative adversarial network (GAN) framework as the base and improved its generator; this approach combines convolutional neural networks (CNNs) and a transformer module to directly generate material decomposition maps from conventional single-energy CT images. Our model pays attention to both local and global information. Then, we compared our method with 6 competitive deep learning methods on water (calcium) and calcium (water) substrate density image datasets. The average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the water (calcium) substrate density images were 32.7207, 0.9685, 0.0323, and 0.0555, respectively. Furthermore, the average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the calcium (water) substrate density images were 30.2823, 0.9449, 0.0652, and 0.0715, respectively. Our model achieved better performance and stronger stability than competing approaches.

Baseline Amide Proton Transfer Imaging at 3T Fails to Predict Early Response to Induction Chemotherapy in Nasopharyngeal Carcinoma.

BACKGROUND: Early identification of nasopharyngeal carcinoma (NPC) patients with high risk of failure to induction chemotherapy (IC) would facilitate prompt individualized treatment decisions and thus reduce toxicity and improve overall survival rate. This study aims to investigate the value of amide proton transfer (APT) imaging in predicting short-term response of NPC to IC and its potential correlation with well-established prognosis-related clinical characteristics. METHODS AND MATERIALS: A total of 80 pathologically confirmed NPC patients receiving pre-treatment APT imaging at 3T were retrospectively enrolled. Using asymmetry analysis, APT maps were calculated with mean (APTmean), 90th percentile (APT90) of APT signals in manually segmented NPC measured. APT values were compared among groups with different histopathological subtypes, clinical stages (namely, T, M, N, and overall stages), EBV-related indices (EBV-DNA), or responses to induction chemotherapy, using Mann-Whitney U test or Kruskal-Wallis H test. RESULTS: NPC showed significantly higher APTmean than normal nasopharyngeal tissues (1.81 ± 0.62% vs.1.32 ± 0.56%, P <0.001). APT signals showed no significant difference between undifferentiated and differentiated NPC subtypes groups, different EBV-DNA groups, or among T, N, M stages and overall clinical stages of II, III, IVA and IVB (all P >0.05). Similarly, baseline APT-related parameters did not differ significantly among different treatment response groups after IC, no matter if evaluated with RECIST criteria or sum volumetric regression ratio (SVRR) (all P >0.05). CONCLUSION: NPC showed significantly stronger APT effect than normal nasopharyngeal tissue, facilitating NPC lesion detection and early identification. However, stationary baseline APT values exhibited no significant correlation with histologic subtypes, clinical stages and EBV-related indices, and showed limited value to predict short-term treatment response to IC.

Contrast-enhanced to noncontrast CT transformation via an adjacency content-transfer-based deep subtraction residual neural network.

To reduce overall patient radiation exposure in some clinical scenarios (since cancer patients need frequent follow-ups), noncontrast CT is not used in some institutions. However, although less desirable, noncontrast CT could provide additional important information. In this article, we propose a deep subtraction residual network based on adjacency content transfer to reconstruct noncontrast CT from contrast CT and maintain image quality comparable to that of a CT scan originally acquired without contrast. To address the slight structural dissimilarity of the paired CT images (noncontrast CT and contrast CT) due to involuntary physiological motion, we introduce a contrastive loss network derived from the adjacency content-transfer strategy. We evaluate the results of various similarity metrics (MSE, SSIM, NRMSE, PSNR, MAE) and the fitting curve (HU distribution) of the output mapping to estimate the reconstruction performance of the algorithm. To build the model, we randomly select a total of 15,405 CT paired images (noncontrast CT and contrast-enhanced CT) for training and 10,270 CT paired images for testing. The proposed algorithm preserves the robust structures from the contrast-enhanced CT scans and learns the noncontrast attenuation pattern from the noncontrast CT scans. During the evaluation, the deep subtraction residual network achieves higher MSE, MAE, NRMSE, and PSNR scores (by 30%) than those of the baseline models (BEGAN, CycleGAN, Pixel2Pixel) and better simulates the HU curve of noncontrast CT attenuation. After validation based on an analysis of the experimental results, we can report that the noncontrast CT images reconstructed by our proposed algorithm not only preserve the high-quality structures from the contrast-enhanced CT images, but also mimic the CT attenuation of the originally acquired noncontrast CT images.