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Model based PET image reconstruction...
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Lin, Yanguang.
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Model based PET image reconstruction and kinetic parameter estimation.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Model based PET image reconstruction and kinetic parameter estimation./
作者:
Lin, Yanguang.
面頁冊數:
192 p.
附註:
Source: Dissertation Abstracts International, Volume: 76-03(E), Section: B.
Contained By:
Dissertation Abstracts International76-03B(E).
標題:
Electrical engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3644678
ISBN:
9781321330304
Model based PET image reconstruction and kinetic parameter estimation.
Lin, Yanguang.
Model based PET image reconstruction and kinetic parameter estimation.
- 192 p.
Source: Dissertation Abstracts International, Volume: 76-03(E), Section: B.
Thesis (Ph.D.)--University of Southern California, 2014.
This item must not be sold to any third party vendors.
Positron Emission Tomography (PET) is a powerful molecular imaging technique that can provide quantitative information about molecular processes in the living body. The system detects pairs of gamma rays emitted from radioactive tracers that are introduced into the living body to minic the behavior of certain biologically active molecules. While having wide clinical and preclinical applications, in particular in oncology and neuroimaging, PET imaging modality faces many challenges. These challenges include low signal to noise ratio (SNR), relatively low spatial and temporal resolution, efficient processing of very large data sets, and difficulty in mapping reconstructed PET images to physiologically meaningful parameters and statistics. The goals of my Ph.D. research are first to use various theoretical and practical approaches to study and improve the SNR and resolution of reconstructed PET images, and also to use statistical models to analyze the reconstructed PET images.
ISBN: 9781321330304Subjects--Topical Terms:
649834
Electrical engineering.
Model based PET image reconstruction and kinetic parameter estimation.
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Positron Emission Tomography (PET) is a powerful molecular imaging technique that can provide quantitative information about molecular processes in the living body. The system detects pairs of gamma rays emitted from radioactive tracers that are introduced into the living body to minic the behavior of certain biologically active molecules. While having wide clinical and preclinical applications, in particular in oncology and neuroimaging, PET imaging modality faces many challenges. These challenges include low signal to noise ratio (SNR), relatively low spatial and temporal resolution, efficient processing of very large data sets, and difficulty in mapping reconstructed PET images to physiologically meaningful parameters and statistics. The goals of my Ph.D. research are first to use various theoretical and practical approaches to study and improve the SNR and resolution of reconstructed PET images, and also to use statistical models to analyze the reconstructed PET images.
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