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Proceedings Paper

Nonlinear PET parametric image reconstruction with MRI information using kernel method
Author(s): Kuang Gong; Guobao Wang; Kevin T. Chen; Ciprian Catana; Jinyi Qi
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Paper Abstract

Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.

Paper Details

Date Published: 9 March 2017
PDF: 7 pages
Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101321G (9 March 2017); doi: 10.1117/12.2254273
Show Author Affiliations
Kuang Gong, Univ. of California, Davis (United States)
Guobao Wang, UC Davis Medical Ctr. (United States)
Kevin T. Chen, Athinoula A. Martinos Ctr. for Biomedical Imaging (United States)
Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Ciprian Catana, Athinoula A. Martinos Ctr. for Biomedical Imaging (United States)
Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Jinyi Qi, Univ. of California, Davis (United States)


Published in SPIE Proceedings Vol. 10132:
Medical Imaging 2017: Physics of Medical Imaging
Thomas G. Flohr; Joseph Y. Lo; Taly Gilat Schmidt, Editor(s)

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