# A Prior-Guided Joint Diffusion Model in Projection Domain for PET Tracer Conversion

arXiv: https://arxiv.org/abs/2506.16733 · HTML: https://mlsanity.com/paper/2506.16733/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise](https://mlsanity.com/paper/2208.09392/) (2022)
- [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022)
- [Denoising Diffusion Bridge Models](https://mlsanity.com/paper/2309.16948/) (2023)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models](https://mlsanity.com/paper/2304.02742/) (2023)
- [Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model](https://mlsanity.com/paper/2305.03901/) (2023)
- [RED: Residual Estimation Diffusion for Low-Dose PET Sinogram Reconstruction](https://mlsanity.com/paper/2411.05354/) (2024)
- [Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation](https://mlsanity.com/paper/2202.06142/) (2022)
- [PET Tracer Conversion among Brain PET via Variable Augmented Invertible Network](https://mlsanity.com/paper/2311.00735/) (2023)
- [st-DTPM: Spatial-Temporal Guided Diffusion Transformer Probabilistic Model for Delayed Scan PET Image Prediction](https://mlsanity.com/paper/2410.22732/) (2024)

## Abstract (arXiv)

Positron emission tomography (PET) is widely used to assess metabolic activity, but its application is limited by the availability of radiotracers. 18F-labeled fluorodeoxyglucose (18F-FDG) is the most commonly used tracer but shows limited effectiveness for certain tumors. In contrast, 6-18F-fluoro-3,4-dihydroxy-L-phenylalanine (18F-DOPA) offers higher specificity for neuroendocrine tumors and neurological disorders. However, the complexity of its synthesis process and constraints on transportation time have limited its clinical application. Among different forms of raw data acquired by the scanner, sinogram is a commonly used representation in PET imaging. Therefore, modeling in projection domain enables more direct utilization of the original information, potentially reducing the accumulation errors during the image reconstruction process. Inspired by these factors, this study proposes a prior-guided joint diffusion model (PJDM) for transforming 18F-FDG PET sinograms into 18F-DOPA PET sinograms. During inference, an initial synthetic 18F-DOPA PET sinogram is first generated using a higher-order hybrid sampler. This sinogram is then degraded and serves as an additional condition to guide the iterative refinement process. Experimental results demonstrated that PJDM effectively improved both sinogram quality and the final synthetic outcomes. The code is available at: https://github.com/yqx7150/PJDM.