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Proceedings Paper

Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method
Author(s): Benjamin Spencer; Jinyi Qi; Ramsey D. Badawi; Guobao Wang
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Paper Abstract

Dynamic PET image reconstruction is a challenging problem because of the ill-conditioned nature of PET and the lowcounting statistics resulted from short time-frames in dynamic imaging. The kernel method for image reconstruction has been developed to improve image reconstruction of low-count PET data by incorporating prior information derived from high-count composite data. In contrast to most of the existing regularization-based methods, the kernel method embeds image prior information in the forward projection model and does not require an explicit regularization term in the reconstruction formula. Inspired by the existing highly constrained back-projection (HYPR) algorithm for dynamic PET image denoising, we propose in this work a new type of kernel that is simpler to implement and further improves the kernel-based dynamic PET image reconstruction. Our evaluation study using a physical phantom scan with synthetic FDG tracer kinetics has demonstrated that the new HYPR kernel-based reconstruction can achieve a better region-of-interest (ROI) bias versus standard deviation trade-off for dynamic PET parametric imaging than the post-reconstruction HYPR denoising method and the previously used nonlocal-means kernel.

Paper Details

Date Published: 9 March 2017
PDF: 7 pages
Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101324W (9 March 2017); doi: 10.1117/12.2254497
Show Author Affiliations
Benjamin Spencer, Univ. of California, Davis Medical Ctr. (United States)
Jinyi Qi, Univ. of California, Davis (United States)
Ramsey D. Badawi, Univ. of California, Davis Medical Ctr. (United States)
Univ. of California, Davis (United States)
Guobao Wang, Univ. of California, Davis Medical Ctr. (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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