A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches

Autor:	Bousse, Alexandre, Kandarpa, Venkata Sai Sundar, Shi, Kuangyu, Gong, Kuang, Lee, Jae Sung, Liu, Chi, Visvikis, Dimitris
Rok vydání:	2023
Předmět:	Physics - Medical Physics
Druh dokumentu:	Working Paper
DOI:	10.1109/TRPMS.2023.3349194
Popis:	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. Comment: 16 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.00232 Zobrazit plný text záznamu View this record from Arxiv