发表机构
Umeå University; Università Campus Bio-Medico di Roma; Università ”Magna Græcia” di Catanzaro; University of Calabria（于默奥大学; 罗马生物医学自由大学; 卡坦扎罗大学; 卡拉布里亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于潜在布朗桥扩散的3D CT到PET转换框架，通过VAE和对比学习对齐潜在空间，在公开数据集上提升PET信号保真度和小病变代谢激活保留。
AI 中文摘要
计算机断层扫描（CT）和正电子发射断层扫描（PET）为癌症诊断和治疗计划提供互补的解剖和功能信息。然而，PET的广泛应用受到高辐射暴露、高昂成本和有限可用性的限制。为解决这些限制，基于深度学习的CT到PET转换已成为一种有前景的方法，可直接从CT图像合成类似PET的信息，尽管准确建模大的跨模态差距仍然具有挑战性。在这项工作中，我们提出了一种基于潜在布朗桥扩散（BBDM）的3D CT到PET转换框架。该方法包括两个阶段。首先，在配对的CT-PET块上训练变分自编码器（VAE），整合对比学习以改善解剖和代谢表示之间的潜在对齐。其次，在潜在空间中训练BBDM，将CT潜在表示转换为相应的PET对应物。然后对转换后的PET潜在变量进行解码和拼接，以重建最终的3D PET体积。我们在两个公开可用的数据集上评估了所提出的方法。基于图像保真度和病变级PET特定指标的定量结果表明，与竞争方法相比，性能有所提高。特别是，所提出的方法提高了PET信号保真度，更好地保留了临床相关的摄取模式，并在保留小病变代谢激活方面表现出改进的性能，为虚拟成像应用铺平了道路。
英文摘要
Computed tomography (CT) and positron emission tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and treatment planning. However, the widespread use of PET is limited by high radiation exposure, elevated costs, and restricted availability. To address these limitations, deep learning-based CT-to-PET translation has emerged as a promising approach for synthesizing PET-like information directly from CT images, although accurately modeling the large cross-modal gap remains challenging. In this work, we propose a 3D CT-to-PET translation framework based on latent Brownian Bridge Diffusion (BBDM). The method consists of two stages. First, a Variational Autoencoder (VAE) is trained on paired CT-PET patches, integrating contrastive learning to improve latent alignment between anatomical and metabolic representations. Second, a BBDM is trained in the latent space to translate CT latent representations into their corresponding PET counterparts. The translated PET latents are then decoded and stitched to reconstruct the final 3D PET volume. We evaluate the proposed approach on two publicly available datasets. Quantitative results based on image fidelity and lesion-level PET-specific metrics demonstrate improved performance compared with competing methods. In particular, the proposed approach improves PET signal fidelity, better preserves clinically relevant uptake patterns, and shows improved performance in preserving small-lesion metabolic activation, paving the way for virtual imaging applications.
