Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.
@article{arxiv.2401.00232,
title = {A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches},
author = {Alexandre Bousse and Venkata Sai Sundar Kandarpa and Kuangyu Shi and Kuang Gong and Jae Sung Lee and Chi Liu and Dimitris Visvikis},
journal= {arXiv preprint arXiv:2401.00232},
year = {2024}
}