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

• URL: http://arxiv.org/abs/2508.16897v2
• Date: Fri, 05 Sep 2025 00:44:03 GMT
• Title: Generating Synthetic Contrast-Enhanced Chest CT Images from Non-Contrast Scans Using Slice-Consistent Brownian Bridge Diffusion Network
• Authors: Pouya Shiri, Xin Yi, Neel P. Mistry, Samaneh Javadinia, Mohammad Chegini, Seok-Bum Ko, Amirali Baniasadi, Scott J. Adams,
• Abstract summary: We propose the first bridge diffusion-based solution for synthesizing contrast-enhanced images from non-contrast CT scans.<n>Unlike conventional slice-wise methods, our framework preserves full 3D anatomical integrity while operating in a high-resolution 2D fashion.
• Score: 4.663250708503928
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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