NDLI: Feature extraction by subspace fitting of time activity curve in PET dynamic studies

Content Provider	IEEE Xplore Digital Library
Author	Huang, C.C. Yu, X. Bading, J. Conti, P.S.
Copyright Year	1998
Description	Author affiliation: Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA (Huang, C.C.)
Abstract	In computer-aided tumor detection, it is important to exploit features which discriminate lesions from normal tissue. Signal subspace is a relatively robust feature and has been used to identify signals in many applications. In this paper, the authors demonstrate that the time activity curves (TACs) of lesions and normal tissue in multi-frame dynamic positron emission tomography (PET) images can be characterized by two distinct subspaces. The subspace fitting techniques used in classical array sensor processing are applied to extract the subspaces of time activity curves associated with lesions and normal tissues, respectively. The MUltiple SIgnal Classification (MUSIC) algorithm and the least squares based subspace fitting method are both investigated. Based on a physiologic compartmental model of tracer kinetics, the authors show that the TACs can be represented by a linear combination of exponential functions. The problem of fitting TACs into a subspace spanned by these exponential functions then turns out to be the well-known problem of finding direction of arrival (DOA) in array signal processing. The two problems differ only in the search range. The results of applying the MUSIC method and least squares based fitting to the clinical dynamic PET data are also shown and compared in this paper.
Starting Page	1721
Ending Page	1725
File Size	504506
Page Count	5
File Format	PDF
ISBN	0780342585
ISSN	10823654
DOI	10.1109/NSSMIC.1997.670649
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-11-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Curve fitting Lesions Positron emission tomography Sensor arrays Multiple signal classification Signal processing Least squares methods Tumors Robustness
Content Type	Text
Resource Type	Article

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