用结构与语义约束提升低剂量造影CT血管重建质量
Structure-constrained Language-informed Diffusion Model for Unpaired Low-dose Computed Tomography Angiography Reconstruction
- 引入结构先验和空间智能实现精准图像生成
- 在无配对数据下仍能保持血管结构一致性
- 适合临床低剂量造影CT图像增强场景
碘对比剂可显著提升CT对多种临床指征的敏感性和特异性，但过量使用可能导致肾损伤或严重过敏反应。深度学习方法可从低剂量对比剂CT生成正常剂量图像，降低辐射剂量同时维持诊断能力。然而，现有方法在无配对图像条件下难以实现精准增强，主要因模型对特定结构识别能力不足。为此，我们提出结构约束的语言引导扩散模型（SLDM），一种整合结构协同与空间智能的统一医学生成模型。首先，有效提取图像结构先验信息以约束模型推理过程，确保增强过程中结构一致性；其次，引入具有空间智能的语义监督策略，融合视觉感知与空间推理功能，促使模型实现精准增强；最后，应用减影血管增强模块，将对比剂区域对比度提升至适宜观察区间。视觉对比定性分析与多指标定量结果均证明，该方法在低剂量对比剂CT血管成像重建中具有显著有效性。
原文摘要 · Abstract (English)
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.
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