학술논문
A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches
Document Type
Periodical
Author
Source
IEEE Transactions on Radiation and Plasma Medical Sciences IEEE Trans. Radiat. Plasma Med. Sci. Radiation and Plasma Medical Sciences, IEEE Transactions on. 8(4):333-347 Apr, 2024
Subject
Language
ISSN
2469-7311
2469-7303
2469-7303
Abstract
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.