3D CT-to-PET Translation via Latent Brownian Bridge Diffusion

2026-09-11

AI Summary

为解决PET成像成本高、辐射强等问题，本文提出基于潜在布朗桥扩散模型的3D CT-to-PET转换框架，通过两阶段训练提高合成PET图像的质量。

Abstract

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.

Problem

• 3D CT-to-PET Translation
• latent Brownian Bridge Diffusion
• cross-modal gap
• deep learning-based synthesis

Innovation

• Latent Brownian Bridge Diffusion
• Contrastive Learning
• Variational Autoencoder
• Cross-modal Translation
• 3D PET Synthesis