NDLI: Lesion enhancement in dynamic positron emission tomography using subspace filtering

Content Provider	IEEE Xplore Digital Library
Author	Hu, C.C. Huang, C.C. Yu, X.
Copyright Year	1999
Description	Author affiliation: Dept. of Electr. Eng., Univ. of Southern California, CA, USA (Hu, C.C.)
Abstract	The high dimensionality and high level noise of dynamic positron emission tomography (PET) data make the identification of the kinetic feature of lesions difficult. Principle component analysis (PCA) is a well-known multivariate image analysis technique to reduce the dimensions and noise level in dynamic PET data. However, the contrast of small lesions is often reduced by PCA, due to the lack of using a priori physiological knowledge of lesions. In this paper, a new method, modified from the generalized sidelobe canceler (GSC), is proposed to incorporate the physiological features extracted from the visible tumor and normal tissue in order to enhance the non-palpable lesions in the background noise. Two different schemes are adopted to implement the modified GSC. One uses both physiological features of lesions and normal tissue, while the other employs only that of lesions. The multidimensional maximum likelihood estimation is applied to find physiological factors which span time activity curve subspaces of lesions and normal tissue from dynamic PET data. Results show that the proposed modified GSC can substantially stretch the contrast of small lesions, and thus is able to aid in detecting non-palpable tumors. The images resulting from post-processing the FBP by the modified GSC are compared with those of the OSEM reconstruction as well as the PCA. The analytical analysis of the method is also described.
Starting Page	1751
Ending Page	1755
File Size	648853
Page Count	5
File Format	PDF
ISBN	0780350219
ISSN	10823654
DOI	10.1109/NSSMIC.1998.773878
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-11-08
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Positron emission tomography Principal component analysis Noise level Image analysis Neoplasms Kinetic theory Feature extraction Data mining Background noise
Content Type	Text
Resource Type	Article

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