# Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement

arXiv: https://arxiv.org/abs/2107.13595 · HTML: https://mlsanity.com/paper/2107.13595/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2021.

## Built on by (most cited first, 6 of 6)

- [Online User Profiling to Detect Social Bots on Twitter](https://mlsanity.com/paper/2203.05966/) (2022)
- [Fully 3D Implementation of the End-to-end Deep Image Prior-based PET Image Reconstruction Using Block Iterative Algorithm](https://mlsanity.com/paper/2212.11844/) (2022)
- [Supervise-assisted Multi-modality Fusion Diffusion Model for PET Restoration](https://mlsanity.com/paper/2602.11545/) (2026)
- [FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising](https://mlsanity.com/paper/2304.00570/) (2023)
- [Whole-body PET image denoising for reduced acquisition time](https://mlsanity.com/paper/2303.16085/) (2023)
- [Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection](https://mlsanity.com/paper/2205.06177/) (2022)

## Abstract (arXiv)

High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. AI models for image denoising and deblurring are becoming increasingly popular for post-reconstruction enhancement of PET images. We present here a detailed review of recent efforts for AI-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of AI models for PET image enhancement, and discuss future directions.