Positron Emission Tomography (PET) and Computed Tomography (CT) are essential for diagnosing, staging, and monitoring various diseases, particularly cancer. Despite their importance, the use of PET/CT systems is limited by the necessity for radioactive materials, the scarcity of PET scanners, and the high cost associated with PET imaging. In contrast, CT scanners are more widely available and significantly less expensive. In response to these challenges, our study addresses the issue of generating PET images from CT images, aiming to reduce both the medical examination cost and the associated health risks for patients. Our contributions are twofold: First, we introduce a conditional diffusion model named CPDM, which, to our knowledge, is one of the initial attempts to employ a diffusion model for translating from CT to PET images. Second, we provide the largest CT-PET dataset to date, comprising 2,028,628 paired CT-PET images, which facilitates the training and evaluation of CT-to-PET translation models. For the CPDM model, we incorporate domain knowledge to develop two conditional maps: the Attention map and the Attenuation map. The former helps the diffusion process focus on areas of interest, while the latter improves PET data correction and ensures accurate diagnostic information. Experimental evaluations across various benchmarks demonstrate that CPDM surpasses existing methods in generating high-quality PET images in terms of multiple metrics. The source code and data samples are available at https://github.com/thanhhff/CPDM.

翻译：正电子发射断层扫描（PET）与计算机断层扫描（CT）对于多种疾病（尤其是癌症）的诊断、分期及监测至关重要。尽管其重要性显著，PET/CT系统的应用仍受限于放射性物质的使用必要性、PET扫描仪的稀缺性以及PET成像的高昂成本。相比之下，CT扫描仪更为普及且成本显著更低。针对这些挑战，本研究致力于解决从CT图像生成PET图像的问题，旨在降低医疗检查成本并减少患者相关的健康风险。我们的贡献主要体现在两方面：首先，我们提出了一种名为CPDM的条件扩散模型，据我们所知，这是将扩散模型应用于CT至PET图像转换的初步尝试之一。其次，我们提供了迄今为止规模最大的CT-PET数据集，包含2,028,628对配对的CT-PET图像，该数据集为CT至PET转换模型的训练与评估提供了支持。对于CPDM模型，我们结合领域知识构建了两种条件映射：注意力图与衰减图。前者有助于扩散过程聚焦于感兴趣区域，而后者则能改进PET数据校正并确保诊断信息的准确性。跨多种基准的实验评估表明，CPDM在多项指标上均优于现有方法，能够生成更高质量的PET图像。源代码与数据样本已公开于https://github.com/thanhhff/CPDM。