# Full-count PET Recovery from Low-count Image Using a Dilated Convolutional Neural Network

arXiv: https://arxiv.org/abs/1910.11865 · HTML: https://mlsanity.com/paper/1910.11865/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 11 of 11)

- [Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance](https://mlsanity.com/paper/2109.00802/) (2021)
- [Full-resolution MLPs Empower Medical Dense Prediction](https://mlsanity.com/paper/2311.16707/) (2023)
- [CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images](https://mlsanity.com/paper/2304.00725/) (2023)
- [TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms](https://mlsanity.com/paper/2308.05365/) (2023)
- [A resource-efficient deep learning framework for low-dose brain PET image reconstruction and analysis](https://mlsanity.com/paper/2202.06548/) (2022)
- [Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling](https://mlsanity.com/paper/2306.04664/) (2023)
- [PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network](https://mlsanity.com/paper/2310.15550/) (2023)
- [MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction](https://mlsanity.com/paper/2406.13150/) (2024)
- [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)
- [Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning](https://mlsanity.com/paper/2604.16925/) (2026)

## Abstract (arXiv)

Positron Emission Tomography (PET) is an essential technique in many clinical applications that allows for quantitative imaging at the molecular level. This study aims to develop a denoising method using novel dilated convolutional neural network to recover full-count images from low-count images. We adopted similar hierarchal structure from the conventional uNet and incorporated dilated kernels in each convolution to allow the network to observe larger, and perhaps, more robust, features within the image. Our dNet were trained alongside a uNet for comparison. Our 2.5D model used a training set (N=30) and testing set (N=5) that were obtained from an ongoing 18F-FDG study. Low-count PET data (10% count) were generated through Poisson thinning from the full listmode file. Both low-count PET and full-count PET were reconstructed with the OSEM algorithm. Objective imaging metrics including mean absolute percent error (MAPE), peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) were used to analyze the denoising methods. Both the uNet and our proposed dNet were successfully trained to synthesize full-count PET images from low-count PET images. Compared to low-count PET, both the uNet and dNet methods significantly improved MAPE, PSNR and SSIM. Our dNet also systematically outperformed uNet on all three metrics and across all testing subjects. This study proposed a novel approach of using dilated convolutions for recovering full-count PET images from low-count PET images. Our dNet significantly outperformed the well-established uNet and demonstrates great potential for denoising low-count PET images.