# TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms

arXiv: https://arxiv.org/abs/2308.05365 · HTML: https://mlsanity.com/paper/2308.05365/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis](https://mlsanity.com/paper/2002.05000/) (2020)
- [DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem](https://mlsanity.com/paper/1804.07851/) (2018)
- [TransCT: Dual-path Transformer for Low Dose Computed Tomography](https://mlsanity.com/paper/2103.00634/) (2021)
- [Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis](https://mlsanity.com/paper/2212.01108/) (2022)
- [Full-count PET Recovery from Low-count Image Using a Dilated Convolutional Neural Network](https://mlsanity.com/paper/1910.11865/) (2019)
- [TransEM:Residual Swin-Transformer based regularized PET image reconstruction](https://mlsanity.com/paper/2205.04204/) (2022)
- [DirectPET: Full Size Neural Network PET Reconstruction from Sinogram Data](https://mlsanity.com/paper/1908.07516/) (2019)

## Built on by (most cited first, 4 of 4)

- [MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction](https://mlsanity.com/paper/2406.13150/) (2024)
- [S3PET: Semi-supervised Standard-dose PET Image Reconstruction via Dose-aware Token Swap](https://mlsanity.com/paper/2407.20878/) (2024)
- [Diffusion Transformer Meets Random Masks: An Advanced PET Reconstruction Framework](https://mlsanity.com/paper/2503.08339/) (2025)
- [UniSino: Physics-Driven Foundational Model for Universal CT Sinogram Standardization](https://mlsanity.com/paper/2508.17816/) (2025)

## Abstract (arXiv)

To obtain high-quality positron emission tomography (PET) images while minimizing radiation exposure, various methods have been proposed for reconstructing standard-dose PET (SPET) images from low-dose PET (LPET) sinograms directly. However, current methods often neglect boundaries during sinogram-to-image reconstruction, resulting in high-frequency distortion in the frequency domain and diminished or fuzzy edges in the reconstructed images. Furthermore, the convolutional architectures, which are commonly used, lack the ability to model long-range non-local interactions, potentially leading to inaccurate representations of global structures. To alleviate these problems, we propose a transformer-based model that unites triple domains of sinogram, image, and frequency for direct PET reconstruction, namely TriDo-Former. Specifically, the TriDo-Former consists of two cascaded networks, i.e., a sinogram enhancement transformer (SE-Former) for denoising the input LPET sinograms and a spatial-spectral reconstruction transformer (SSR-Former) for reconstructing SPET images from the denoised sinograms. Different from the vanilla transformer that splits an image into 2D patches, based specifically on the PET imaging mechanism, our SE-Former divides the sinogram into 1D projection view angles to maintain its inner-structure while denoising, preventing the noise in the sinogram from prorogating into the image domain. Moreover, to mitigate high-frequency distortion and improve reconstruction details, we integrate global frequency parsers (GFPs) into SSR-Former. The GFP serves as a learnable frequency filter that globally adjusts the frequency components in the frequency domain, enforcing the network to restore high-frequency details resembling real SPET images. Validations on a clinical dataset demonstrate that our TriDo-Former outperforms the state-of-the-art methods qualitatively and quantitatively.