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Proceedings Paper

Quantization of liver tissue in dual kVp computed tomography using linear discriminant analysis
Author(s): J. Eric Tkaczyk; David Langan; Xiaoye Wu; Daniel Xu; Thomas Benson; Jed D. Pack; Andrea Schmitz; Amy Hara; William Palicek; Paul Licato; Jaynne Leverentz
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Paper Abstract

Linear discriminate analysis (LDA) is applied to dual kVp CT and used for tissue characterization. The potential to quantitatively model both malignant and benign, hypo-intense liver lesions is evaluated by analysis of portal-phase, intravenous CT scan data obtained on human patients. Masses with an a priori classification are mapped to a distribution of points in basis material space. The degree of localization of tissue types in the material basis space is related to both quantum noise and real compositional differences. The density maps are analyzed with LDA and studied with system simulations to differentiate these factors. The discriminant analysis is formulated so as to incorporate the known statistical properties of the data. Effective kVp separation and mAs relates to precision of tissue localization. Bias in the material position is related to the degree of X-ray scatter and partial-volume effect. Experimental data and simulations demonstrate that for single energy (HU) imaging or image-based decomposition pixel values of water-like tissues depend on proximity to other iodine-filled bodies. Beam-hardening errors cause a shift in image value on the scale of that difference sought between in cancerous and cystic lessons. In contrast, projection-based decomposition or its equivalent when implemented on a carefully calibrated system can provide accurate data. On such a system, LDA may provide novel quantitative capabilities for tissue characterization in dual energy CT.

Paper Details

Date Published: 10 March 2009
PDF: 12 pages
Proc. SPIE 7258, Medical Imaging 2009: Physics of Medical Imaging, 72580G (10 March 2009); doi: 10.1117/12.811374
Show Author Affiliations
J. Eric Tkaczyk, GE Global Research (United States)
David Langan, GE Global Research (United States)
Xiaoye Wu, GE Global Research (United States)
Daniel Xu, GE Global Research (United States)
Thomas Benson, GE Global Research (United States)
Jed D. Pack, GE Global Research (United States)
Andrea Schmitz, GE Global Research (United States)
Amy Hara, Mayo Clinic Scottsdale (United States)
William Palicek, Mayo Clinic Scottsdale (United States)
Paul Licato, GE Healthcare (United States)
Jaynne Leverentz, GE Healthcare (United States)


Published in SPIE Proceedings Vol. 7258:
Medical Imaging 2009: Physics of Medical Imaging
Ehsan Samei; Jiang Hsieh, Editor(s)

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