Assessment of Deep-Learning Based Motion Compensation on Detection of Perfusion Defects in Cardiac-Gated SPECT Images-Reference-Cited by-同舟云学术

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Assessment of Deep-Learning Based Motion Compensation on Detection of Perfusion Defects in Cardiac-Gated SPECT Images

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
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Author:

Zhang Xirang¹,Yang Yongyi¹,Pretorius P. Hendrik¹,King Michael A.¹

Affiliation:

1. Illinois Institute of Technology,Department of Electrical and Computer Engineering,Chicago,IL,USA,60616

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230414.pdf?arnumber=10230414

Reference20 articles.

1. Automated quantification of myocardial perfusion SPECT using simplified normal limits

2. Quantitation in gated perfusion SPECT imaging: The Cedars-Sinai approach

3. Fully 4D motion-compensated reconstruction of cardiac SPECT images

4. Polar Presentation of Coronary Angiography and Thallium-201 Single Photon Emission Computed Tomography

5. Comparing Image Detection Algorithms Using Resampling

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT;IEEE Transactions on Radiation and Plasma Medical Sciences;2024-04

2. Exploring Anatomical Similarity in Cardiac-Gated Spect Images for A Deep Learning Network;2023 IEEE International Conference on Image Processing (ICIP);2023-10-08

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