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Methods Inf Med 1997; 36(04/05): 329-331
DOI: 10.1055/s-0038-1636869
Original Article
Schattauer GmbH

Neural Network-Based PET Image Reconstruction

Y. Kosugi
1   Tokyo Institute of Technology, Yokohama, Japan
,
M. Sase
2   University of Tokyo, School of Medicine, Tokyo, Japan
,
Y. Suganami
1   Tokyo Institute of Technology, Yokohama, Japan
,
N. Uemoto
1   Tokyo Institute of Technology, Yokohama, Japan
,
T. Momose
2   University of Tokyo, School of Medicine, Tokyo, Japan
,
J. Nishikawa
2   University of Tokyo, School of Medicine, Tokyo, Japan
› Author Affiliations
Further Information

Publication History

Publication Date:
19 February 2018 (online)

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Abstract:

In PET image analysis, conventional deconvolution alone will not give sufficient information for a precise study of a localized brain function. In the deconvolution process, which is a type of inverse problem, it is important to confine the solution space by incorporating a priori knowledge such as the tissue distribution given by MR images as well as smoothness in the blood flow distribution profile. An MR-embedded neural-network model is described to reduce the partial volume effect in the restoration of blood flow profiles from PET images.

Keywords:

Neural Networks - Positron Emission Tomography - Inverse Problem - Regularization
 

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