# Machine Learning in Quantitative PET Imaging

arXiv: https://arxiv.org/abs/2001.06597 · HTML: https://mlsanity.com/paper/2001.06597/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning](https://mlsanity.com/paper/2604.16925/) (2026)

## Abstract (arXiv)

This paper reviewed the machine learning-based studies for quantitative positron emission tomography (PET). Specifically, we summarized the recent developments of machine learning-based methods in PET attenuation correction and low-count PET reconstruction by listing and comparing the proposed methods, study designs and reported performances of the current published studies with brief discussion on representative studies. The contributions and challenges among the reviewed studies were summarized and highlighted in the discussion part followed by.