# Does prior knowledge in the form of multiple low-dose PET images (at different dose levels) improve standard-dose PET prediction?

arXiv: https://arxiv.org/abs/2202.10998 · HTML: https://mlsanity.com/paper/2202.10998/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018)
- [On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task](https://mlsanity.com/paper/1707.01992/) (2017)
- [Deep learning-based synthetic CT generation from MR images: comparison of generative adversarial and residual neural networks](https://mlsanity.com/paper/2103.01609/) (2021)
- [Deep learning-based noise reduction in low dose SPECT Myocardial Perfusion Imaging: Quantitative assessment and clinical performance](https://mlsanity.com/paper/2103.11974/) (2021)

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

- [Whole-body PET image denoising for reduced acquisition time](https://mlsanity.com/paper/2303.16085/) (2023)

## Abstract (arXiv)

Reducing the injected dose would result in quality degradation and loss of information in PET imaging. To address this issue, deep learning methods have been introduced to predict standard PET images (S-PET) from the corresponding low-dose versions (L-PET). The existing deep learning-based denoising methods solely rely on a single dose level of PET images to predict the S-PET images. In this work, we proposed to exploit the prior knowledge in the form of multiple low-dose levels of PET images (in addition to the target low-dose level) to estimate the S-PET images.