Generating Synthetic Contrast-Enhanced Chest CT Images from Non-Contrast Scans Using Slice-Consistent Brownian Bridge Diffusion Network

Contrast-enhanced computed tomography (CT) imaging is essential for diagnosing and monitoring thoracic diseases, including aortic pathologies. However, contrast agents pose risks such as nephrotoxicity and allergic-like reactions. The ability to generate high-fidelity synthetic contrast-enhanced CT angiography (CTA) images without contrast administration would be transformative, enhancing patient safety and accessibility while reducing healthcare costs. In this study, we propose the first bridge diffusion-based solution for synthesizing contrast-enhanced CTA images from non-contrast CT scans. Our approach builds on the Slice-Consistent Brownian Bridge Diffusion Model (SC-BBDM), leveraging its ability to model complex mappings while maintaining consistency across slices. Unlike conventional slice-wise synthesis methods, our framework preserves full 3D anatomical integrity while operating in a high-resolution 2D fashion, allowing seamless volumetric interpretation under a low memory budget. To ensure robust spatial alignment, we implement a comprehensive preprocessing pipeline that includes resampling, registration using the Symmetric Normalization method, and a sophisticated dilated segmentation mask to extract the aorta and surrounding structures. We create two datasets from the Coltea-Lung dataset: one containing only the aorta and another including both the aorta and heart, enabling a detailed analysis of anatomical context. We compare our approach against baseline methods on both datasets, demonstrating its effectiveness in preserving vascular structures while enhancing contrast fidelity.

PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier

Dental caries is one of the most chronic diseases involving the majority of the population during their lifetime. Caries lesions are typically diagnosed by radiologists relying only on their visual inspection to detect via dental x-rays. In many cases, dental caries is hard to identify using x-rays and can be misinterpreted as shadows due to different reasons such as low image quality. Hence, developing a decision support system for caries detection has been a topic of interest in recent years. Here, we propose an automatic diagnosis system to detect dental caries in Panoramic images for the first time, to the best of authors' knowledge. The proposed model benefits from various pretrained deep learning models through transfer learning to extract relevant features from x-rays and uses a capsule network to draw prediction results. On a dataset of 470 Panoramic images used for features extraction, including 240 labeled images for classification, our model achieved an accuracy score of 86.05\% on the test set. The obtained score demonstrates acceptable detection performance and an increase in caries detection speed, as long as the challenges of using Panoramic x-rays of real patients are taken into account. Among images with caries lesions in the test set, our model acquired recall scores of 69.44\% and 90.52\% for mild and severe ones, confirming the fact that severe caries spots are more straightforward to detect and efficient mild caries detection needs a more robust and larger dataset. Considering the novelty of current research study as using Panoramic images, this work is a step towards developing a fully automated efficient decision support system to assist domain experts.