Structure-constrained Language-informed Diffusion Model for Unpaired Low-dose Computed Tomography Angiography Reconstruction

2026-01-28

AI Summary

This study addresses the challenge of limited reconstruction quality in low-dose iodinated contrast-enhanced CT angiography (CTA), which stems from the absence of paired training data and difficulties in accurately modeling vascular anatomy. To overcome these limitations, the authors propose a structure-constrained and language-guided diffusion model that integrates structural priors, spatial semantic supervision, and a subtraction-based vascular enhancement module. Notably, this work introduces language-guided spatial semantic constraints for the first time in unpaired low-dose CTA reconstruction, enabling high-fidelity, anatomy-preserving vascular enhancement without requiring paired data. The method significantly improves vessel contrast and diagnostic usability, consistently outperforming existing approaches across multiple quantitative metrics and visual assessments.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The application of iodinated contrast media (ICM) improves the sensitivity and specificity of computed tomography (CT) for a wide range of clinical indications. However, overdose of ICM can cause problems such as kidney damage and life-threatening allergic reactions. Deep learning methods can generate CT images of normal-dose ICM from low-dose ICM, reducing the required dose while maintaining diagnostic power. However, existing methods are difficult to realize accurate enhancement with incompletely paired images, mainly because of the limited ability of the model to recognize specific structures. To overcome this limitation, we propose a Structure-constrained Language-informed Diffusion Model (SLDM), a unified medical generation model that integrates structural synergy and spatial intelligence. First, the structural prior information of the image is effectively extracted to constrain the model inference process, thus ensuring structural consistency in the enhancement process. Subsequently, semantic supervision strategy with spatial intelligence is introduced, which integrates the functions of visual perception and spatial reasoning, thus prompting the model to achieve accurate enhancement. Finally, the subtraction angiography enhancement module is applied, which serves to improve the contrast of the ICM agent region to suitable interval for observation. Qualitative analysis of visual comparison and quantitative results of several metrics demonstrate the effectiveness of our method in angiographic reconstruction for low-dose contrast medium CT angiography.

Problem

• low-dose CT angiography
• unpaired image reconstruction
• structure recognition
• contrast medium reduction
• image enhancement

Innovation

• Structure-constrained Diffusion Model
• Language-informed Generation
• Unpaired CT Angiography Reconstruction
• Spatial Intelligence
• Low-dose Contrast Enhancement