Bousse, Alexandre; Kandarpa, Venkata Sai Sundar; Shi, Kuangyu; Gong, Kuang; Lee, Jae Sung; Liu, Chi; Visvikis, Dimitris (15 January 2024). A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches. ArXiv
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nihpp-2401.00232v2.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (2MB) | Preview |
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.
Item Type: |
Working Paper |
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Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Clinic of Nuclear Medicine |
UniBE Contributor: |
Shi, Kuangyu |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2331-8422 |
Publisher: |
ArXiv |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
14 Feb 2024 09:27 |
Last Modified: |
15 Feb 2024 15:54 |
PubMed ID: |
38313194 |
Uncontrolled Keywords: |
Deep Learning Low-Dose PET SPECT |
BORIS DOI: |
10.48350/192703 |
URI: |
https://boris.unibe.ch/id/eprint/192703 |
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