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.

翻译：碘化对比剂（ICM）的应用显著提升了计算机断层扫描（CT）对多种临床适应症的敏感性与特异性。然而，ICM过量使用可能导致肾损伤及危及生命的过敏反应等问题。深度学习方法能够从低剂量ICM图像生成正常剂量ICM的CT图像，在保持诊断效能的同时降低所需剂量。然而，现有方法难以在非完全配对图像条件下实现精准增强，主要源于模型识别特定结构的能力受限。为突破此限制，本文提出结构约束语言引导扩散模型（SLDM），这是一种融合结构协同与空间智能的统一医学生成模型。首先，有效提取图像的结构先验信息以约束模型推理过程，从而确保增强过程中的结构一致性。随后，引入具备空间智能的语义监督策略，该策略整合了视觉感知与空间推理功能，进而促使模型实现精准增强。最后，应用减影血管造影增强模块，其作用在于将ICM药剂区域的对比度提升至适宜观察的区间。视觉对比的定性分析与多项指标的定量结果均证明，本方法在低剂量对比剂CT血管造影的血管造影重建中具有显著有效性。