Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

EJNMMI Physics

Review

Nikolaos I. Papandrianos¹  Anna Feleki¹  Elpiniki I. Papageorgiou¹  Serafeim Moustakidis²  Ioannis D. Apostolopoulos³ 

[1] Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500, Larisa, Greece;Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500, Larisa, Greece;AIDEAS OÜ, 10117, Tallinn, Estonia;Department of Medical Physics, School of Medicine, University of Patras, 26504, Patras, Greece;Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500, Larisa, Greece;

关键词: Deep learning;    Cardiovascular diseases;    Nuclear medicine;    SPECT;    Artificial intelligence;   

DOI  :  10.1186/s40658-022-00522-7

 received in 2022-08-29, accepted in 2022-12-19,  发布年份 2022

来源: Springer

【 摘 要 】

Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are major image acquisition technologies in nuclear medicine. Though several studies have been conducted to apply DL in many nuclear medicine domains, such as cancer detection and classification, few studies have employed such methods for cardiovascular disease applications. The present paper reviews recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed. Current limitations of DL approaches and future research directions are discussed.

【 授权许可】

CC BY   
© The Author(s) 2023

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