# 200x Low-dose PET Reconstruction using Deep Learning

arXiv: https://arxiv.org/abs/1712.04119 · HTML: https://mlsanity.com/paper/1712.04119/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2017.

## Built on by (most cited first, 18 of 18)

- [Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV](https://mlsanity.com/paper/2407.11087/) (2024)
- [Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data](https://mlsanity.com/paper/2107.09892/) (2021)
- [Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction](https://mlsanity.com/paper/2308.10157/) (2023)
- [Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging](https://mlsanity.com/paper/2106.12175/) (2021)
- [3D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesis](https://mlsanity.com/paper/2205.08887/) (2022)
- [DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging](https://mlsanity.com/paper/2311.04248/) (2023)
- [Deep unfolding as iterative regularization for imaging inverse problems](https://mlsanity.com/paper/2211.13452/) (2022)
- [Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data](https://mlsanity.com/paper/2405.12996/) (2024)
- [Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction](https://mlsanity.com/paper/2407.00944/) (2024)
- [SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation](https://mlsanity.com/paper/2411.12755/) (2024)
- [PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network](https://mlsanity.com/paper/2310.15550/) (2023)
- [Diffusion Transformer Meets Random Masks: An Advanced PET Reconstruction Framework](https://mlsanity.com/paper/2503.08339/) (2025)
- [Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI](https://mlsanity.com/paper/2205.04044/) (2022)
- [Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction](https://mlsanity.com/paper/2409.06198/) (2024)
- [End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms](https://mlsanity.com/paper/2412.03617/) (2024)
- [MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising](https://mlsanity.com/paper/2505.05112/) (2025)
- [Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction](https://mlsanity.com/paper/2509.00395/) (2025)
- [DECADE: A Temporally-Consistent Unsupervised Diffusion Model for Enhanced Rb-82 Dynamic Cardiac PET Image Denoising](https://mlsanity.com/paper/2603.07759/) (2026)

## Abstract (arXiv)

Positron emission tomography (PET) is widely used in various clinical applications, including cancer diagnosis, heart disease and neuro disorders. The use of radioactive tracer in PET imaging raises concerns due to the risk of radiation exposure. To minimize this potential risk in PET imaging, efforts have been made to reduce the amount of radio-tracer usage. However, lowing dose results in low Signal-to-Noise-Ratio (SNR) and loss of information, both of which will heavily affect clinical diagnosis. Besides, the ill-conditioning of low-dose PET image reconstruction makes it a difficult problem for iterative reconstruction algorithms. Previous methods proposed are typically complicated and slow, yet still cannot yield satisfactory results at significantly low dose. Here, we propose a deep learning method to resolve this issue with an encoder-decoder residual deep network with concatenate skip connections. Experiments shows the proposed method can reconstruct low-dose PET image to a standard-dose quality with only two-hundredth dose. Different cost functions for training model are explored. Multi-slice input strategy is introduced to provide the network with more structural information and make it more robust to noise. Evaluation on ultra-low-dose clinical data shows that the proposed method can achieve better result than the state-of-the-art methods and reconstruct images with comparable quality using only 0.5% of the original regular dose.