Deep Learning Image Denoising in PET: Quantitative Impact on Kinetic Modeling and Clinical Metrics-Reference-Cited by-同舟云学术

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Deep Learning Image Denoising in PET: Quantitative Impact on Kinetic Modeling and Clinical Metrics

Published:2023-11-04 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD)
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Author:

Muller F.¹,Li E.¹,Gao M.¹,Pantel A.²,Parma M.¹,Surti S.¹,Vanhove C.³,Vandenberghe S.³,Daube-Witherspoon M. E.¹,Karp J. S.¹

Affiliation:

1. University of Pennsylvania,Department of Radiology,Philadelphia,Pennsylvania,United States of America

2. Hospital of UPenn,Division of Nuclear Medicine,Philadelphia,Pennsylvania,United States of America

3. Ghent University,Faculty of Engineering and Architecture,Ghent,Belgium

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10337770/10337807/10338459.pdf?arnumber=10338459

Reference2 articles.

1. Imaging Cancer Metabolism: Underlying Biology and Emerging Strategies

2. Determination of Accuracy and Precision of Lesion Uptake Measurements in Human Subjects with Time-of-Flight PET

Cited by 1 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. Current progress and future perspectives in total‐body PET imaging, part I: Data processing and analysis;iRADIOLOGY;2024-03-11

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