# TransEM:Residual Swin-Transformer based regularized PET image reconstruction

arXiv: https://arxiv.org/abs/2205.04204 · HTML: https://mlsanity.com/paper/2205.04204/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2022.

## Built on by (most cited first, 10 of 10)

- [Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction](https://mlsanity.com/paper/2407.00944/) (2024)
- [TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms](https://mlsanity.com/paper/2308.05365/) (2023)
- [Whole-body PET image denoising for reduced acquisition time](https://mlsanity.com/paper/2303.16085/) (2023)
- [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)
- [DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction](https://mlsanity.com/paper/2303.04661/) (2023)
- [STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction](https://mlsanity.com/paper/2303.04667/) (2023)
- [Deep unrolled primal dual network for TOF-PET list-mode image reconstruction](https://mlsanity.com/paper/2410.11148/) (2024)
- [End-to-End PET Image Reconstruction via a Posterior-Mean Diffusion Model](https://mlsanity.com/paper/2503.08546/) (2025)
- [FlowPET: Physics-Informed Symplectic Flow Matching for Low-Count PET Reconstruction](https://mlsanity.com/paper/2607.11104/) (2026)

## Abstract (arXiv)

Positron emission tomography(PET) image reconstruction is an ill-posed inverse problem and suffers from high level of noise due to limited counts received. Recently deep neural networks especially convolutional neural networks(CNN) have been successfully applied to PET image reconstruction. However, the local characteristics of the convolution operator potentially limit the image quality obtained by current CNN-based PET image reconstruction methods. In this paper, we propose a residual swin-transformer based regularizer(RSTR) to incorporate regularization into the iterative reconstruction framework. Specifically, a convolution layer is firstly adopted to extract shallow features, then the deep feature extraction is accomplished by the swin-transformer layer. At last, both deep and shallow features are fused with a residual operation and another convolution layer. Validations on the realistic 3D brain simulated low-count data show that our proposed method outperforms the state-of-the-art methods in both qualitative and quantitative measures.