标题： 从非对比扫描生成合成对比增强胸部CT图像的切片一致布朗桥扩散网络 显示英文标题

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

摘要： 增强计算机断层扫描（CT）成像是诊断和监测胸部疾病，包括主动脉病变的重要手段。 然而，对比剂会带来诸如肾毒性及类似过敏反应等风险。 能够在不使用对比剂的情况下生成高保真度的合成增强CT血管造影（CTA）图像将具有变革性，从而提高患者安全性和可及性，同时降低医疗成本。 在本研究中，我们提出了第一个基于桥接扩散的方法，用于从非增强CT扫描中合成增强CTA图像。 我们的方法建立在切片一致的布朗桥扩散模型（SC-BBDM）之上，利用其建模复杂映射的能力，同时保持切片间的连贯性。 与传统的逐切片合成方法不同，我们的框架在保持完整三维解剖完整性的同时，以高分辨率二维方式运行，使得在低内存预算下能够无缝进行体积解释。 为了确保稳健的空间对齐，我们实现了一个全面的预处理流程，包括重采样、使用对称归一化方法进行配准，以及一个复杂的扩张分割掩码，以提取主动脉及其周围结构。 我们从Coltea-Lung数据集中创建了两个数据集：一个仅包含主动脉，另一个同时包含主动脉和心脏，从而能够对解剖上下文进行详细分析。 我们在两个数据集上将我们的方法与基线方法进行了比较，证明了其在保留血管结构的同时增强对比度保真度的有效性。 显示英文摘要

摘要： 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.