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

2025-08-23

AI Summary

This study addresses the clinical need for high-fidelity synthetic contrast-enhanced chest CT angiography (CTA) without iodinated contrast agents—mitigating risks of allergic reactions and nephrotoxicity while improving accessibility in resource-limited settings. To this end, we propose SC-BBDM (Slice-Consistent Brownian Bridge Diffusion Model), the first diffusion-based framework explicitly designed for 3D thoracic CT. SC-BBDM enforces inter-slice continuity and anatomical consistency via symmetric normalization-based registration, dilated vessel mask guidance, and isotropic resampling. Evaluated on two in-house multicenter datasets, SC-BBDM significantly outperforms existing non-registration and non-diffusion baselines, achieving state-of-the-art performance in vascular structural integrity, contrast fidelity, and volume-rendering quality. Our approach establishes a novel paradigm for contrast-free CTA clinical translation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Generating synthetic contrast-enhanced CT images without contrast agents
• Preserving 3D anatomical integrity while operating in 2D fashion
• Enhancing patient safety by eliminating contrast agent risks

Innovation

• Brownian Bridge Diffusion for contrast synthesis
• Slice-consistent 3D modeling with 2D efficiency
• Symmetric Normalization registration for spatial alignment