# End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms

arXiv: https://arxiv.org/abs/2412.03617 · HTML: https://mlsanity.com/paper/2412.03617/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 129 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 26 of 26)

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- [200x Low-dose PET Reconstruction using Deep Learning](https://mlsanity.com/paper/1712.04119/) (2017)
- [Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance](https://mlsanity.com/paper/2109.00802/) (2021)
- [Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images](https://mlsanity.com/paper/2106.15395/) (2021)
- [Guiding InfoGAN with Semi-Supervision](https://mlsanity.com/paper/1707.04487/) (2017)
- [A Noise-level-aware Framework for PET Image Denoising](https://mlsanity.com/paper/2203.08034/) (2022)

## Abstract (arXiv)

As a sensitive functional imaging technique, positron emission tomography (PET) plays a critical role in early disease diagnosis. However, obtaining a high-quality PET image requires injecting a sufficient dose (standard dose) of radionuclides into the body, which inevitably poses radiation hazards to patients. To mitigate radiation hazards, the reconstruction of standard-dose PET (SPET) from low-dose PET (LPET) is desired. According to imaging theory, PET reconstruction process involves multiple domains (e.g., projection domain and image domain), and a significant portion of the difference between SPET and LPET arises from variations in the noise levels introduced during the sampling of raw data as sinograms. In light of these two facts, we propose an end-to-end TriPle-domain LPET EnhancemenT (TriPLET) framework, by leveraging the advantages of a hybrid denoising-and-reconstruction process and a triple-domain representation (i.e., sinograms, frequency spectrum maps, and images) to reconstruct SPET images from LPET sinograms. Specifically, TriPLET consists of three sequentially coupled components including 1) a Transformer-assisted denoising network that denoises the inputted LPET sinograms in the projection domain, 2) a discrete-wavelet-transform-based reconstruction network that further reconstructs SPET from LPET in the wavelet domain, and 3) a pair-based adversarial network that evaluates the reconstructed SPET images in the image domain. Extensive experiments on the real PET dataset demonstrate that our proposed TriPLET can reconstruct SPET images with the highest similarity and signal-to-noise ratio to real data, compared with state-of-the-art methods.