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

arXiv: https://arxiv.org/abs/2609.12860 · HTML: https://mlsanity.com/paper/2609.12860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022)
- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection](https://mlsanity.com/paper/1802.07846/) (2018)
- [A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising](https://mlsanity.com/paper/2304.05359/) (2023)
- [XGeM: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation](https://mlsanity.com/paper/2501.04614/) (2025)
- [Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin](https://mlsanity.com/paper/2503.15555/) (2025)
- [CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach](https://mlsanity.com/paper/2410.21932/) (2024)

## Abstract (arXiv)

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.