Cross-modal PET-to-CT translation is an important problem in medical image computing. PET encodes the functional distribution of a radiotracer, whereas CT encodes anatomical electron density, and the two are separated by a substantial modality gap. Existing approaches have well-known limitations: generative adversarial networks (GANs) suffer from unstable training and anatomical artifacts, while standard denoising diffusion probabilistic models (DDPMs) converge slowly because the reverse process starts from pure noise. In this work, we systematically evaluate the Brownian Bridge diffusion model (BBDM) for prostate-specific membrane antigen (PSMA) PET-to-CT translation under a patient-disjoint experimental setting. BBDM constructs a stochastic bridge between the source and target domains, so that the reverse diffusion trajectory is initialized from the PET source endpoint rather than from pure Gaussian noise. The processed dataset was partitioned at the patient level into 287 training patients, 32 validation patients, and 35 independent test patients. Quantitative evaluation was performed on 3741 test slices using complementary image-quality metrics, and statistical significance was assessed using patient-level MAE values. Compared with the matched DDPM baseline using the same UNet backbone and training budget, BBDM achieved substantially lower reconstruction error, supporting the utility of source-anchored bridge diffusion relative to conventional noise-initialized diffusion for PET-to-CT synthesis. After adding SelfRDB-L1 as a recent diffusion-bridge baseline, BBDM remained competitive across the broader metric set, although SelfRDB-L1 achieved a lower patient-level MAE. These results suggest that BBDM is a competitive bridge-based formulation for PET-conditioned CT synthesis, while further clinical validation is required before diagnostic use.
- Open Access
- Article
A Pilot Study on PET-to-CT Cross-Modal Translation: Systematic Validation of Brownian Bridge Diffusion Models
- Xiaoyang Gu,
- Tianyu Shi *
Author Information
Received: 25 Jun 2026 | Revised: 21 Aug 2026 | Accepted: 02 Sep 2026 | Published: 20 Sep 2026
Abstract
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