# A robust multi-domain network for short-scanning amyloid PET reconstruction

arXiv: https://arxiv.org/abs/2305.09986 · HTML: https://mlsanity.com/paper/2305.09986/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [Learning Multi-Domain Convolutional Neural Networks for Visual Tracking](https://mlsanity.com/paper/1510.07945/) (2015)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)

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

- [MGMAR: Metal-Guided Metal Artifact Reduction for X-ray Computed Tomography](https://mlsanity.com/paper/2603.13447/) (2026)

## Abstract (arXiv)

This paper presents a robust multi-domain network designed to restore low-quality amyloid PET images acquired in a short period of time. The proposed method is trained on pairs of PET images from short (2 minutes) and standard (20 minutes) scanning times, sourced from multiple domains. Learning relevant image features between these domains with a single network is challenging. Our key contribution is the introduction of a mapping label, which enables effective learning of specific representations between different domains. The network, trained with various mapping labels, can efficiently correct amyloid PET datasets in multiple training domains and unseen domains, such as those obtained with new radiotracers, acquisition protocols, or PET scanners. Internal, temporal, and external validations demonstrate the effectiveness of the proposed method. Notably, for external validation datasets from unseen domains, the proposed method achieved comparable or superior results relative to methods trained with these datasets, in terms of quantitative metrics such as normalized root mean-square error and structure similarity index measure. Two nuclear medicine physicians evaluated the amyloid status as positive or negative for the external validation datasets, with accuracies of 0.970 and 0.930 for readers 1 and 2, respectively.