Clinical Feasibility of Deep Learning-Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT.

Journal: Clinical nuclear medicine

PMID: 38409758

Abstract

PURPOSE: We aimed to develop deep learning (DL)-based attenuation correction models for Tl-201 myocardial perfusion SPECT (MPS) images and evaluate their clinical feasibility.

Authors

Sungjoo Lim

From the Department of Biomedical Systems Informatics, Yonsei University, Seoul.
Yong-Jin Park
Su Jin Lee

Nuclear Medicine, Ajou University School of Medicine, Suwon, Republic of Korea.
Young-Sil An

Department of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon, South Korea.
Joon-Kee Yoon

Department of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon, South Korea.

Keywords

Aged Deep Learning Feasibility Studies Female Humans Image Processing, Computer-Assisted Male Middle Aged Perfusion Retrospective Studies Sensitivity and Specificity Thallium Radioisotopes Tomography, Emission-Computed, Single-Photon

External Resources

View on PubMed Access via DOI PubMed (38409758)

Clinical Feasibility of Deep Learning-Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT.

Abstract

Authors

Keywords

External Resources

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