# Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction

arXiv: https://arxiv.org/abs/2407.00944 · HTML: https://mlsanity.com/paper/2407.00944/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [DiffIR: Efficient Diffusion Model for Image Restoration](https://mlsanity.com/paper/2303.09472/) (2023)
- [PET image denoising based on denoising diffusion probabilistic models](https://mlsanity.com/paper/2209.06167/) (2022)
- [Iterative PET Image Reconstruction Using Convolutional Neural Network Representation](https://mlsanity.com/paper/1710.03344/) (2017)
- [200x Low-dose PET Reconstruction using Deep Learning](https://mlsanity.com/paper/1712.04119/) (2017)
- [TransEM:Residual Swin-Transformer based regularized PET image reconstruction](https://mlsanity.com/paper/2205.04204/) (2022)

## Built on by (most cited first, 5 of 5)

- [Diffusion Transformer Meets Random Masks: An Advanced PET Reconstruction Framework](https://mlsanity.com/paper/2503.08339/) (2025)
- [GR-Diffusion: 3D Gaussian Representation Meets Diffusion in Whole-Body PET Reconstruction](https://mlsanity.com/paper/2602.11653/) (2026)
- [UniSino: Physics-Driven Foundational Model for Universal CT Sinogram Standardization](https://mlsanity.com/paper/2508.17816/) (2025)
- [Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction](https://mlsanity.com/paper/2509.00395/) (2025)
- [Meta-information Guided Cross-domain Synergistic Diffusion Model for Low-dose PET Reconstruction](https://mlsanity.com/paper/2512.22237/) (2025)

## Abstract (arXiv)

Positron emission tomography (PET) is an advanced medical imaging technique that plays a crucial role in non-invasive clinical diagnosis. However, while reducing radiation exposure through low-dose PET scans is beneficial for patient safety, it often results in insufficient statistical data. This scarcity of data poses significant challenges for accurately reconstructing high-quality images, which are essential for reliable diagnostic outcomes. In this research, we propose a diffusion transformer model (DTM) guided by joint compact prior (JCP) to enhance the reconstruction quality of low-dose PET imaging. In light of current research findings, we present a pioneering PET reconstruction model that integrates diffusion and transformer models for joint optimization. This model combines the powerful distribution mapping abilities of diffusion models with the capacity of transformers to capture long-range dependencies, offering significant advantages for low-dose PET reconstruction. Additionally, the incorporation of the lesion refining block and penalized weighted least squares (PWLS) enhance the recovery capability of lesion regions and preserves detail information, solving blurring problems in lesion areas and texture details of most deep learning frameworks. Experimental results demonstrate the effectiveness of DTM in enhancing image quality and preserving critical clinical information for low-dose PET scans. Our approach not only reduces radiation exposure risks but also provides a more reliable PET imaging tool for early disease detection and patient management.