Quantization of liver tissue in dual kVp computed tomography using linear discriminant analysis

J. Eric Tkaczyk; David Langan; Xiaoye Wu; Daniel Xu; Thomas Benson; Jed D. Pack; Andrea Schmitz; Amy Hara; William Palicek; Paul Licato; Jaynne Leverentz

doi:10.1117/12.811374

10 March 2009 Quantization of liver tissue in dual kVp computed tomography using linear discriminant analysis

J. Eric Tkaczyk, David Langan, Xiaoye Wu, Daniel Xu, Thomas Benson, Jed D. Pack, Andrea Schmitz, Amy Hara, William Palicek, Paul Licato, Jaynne Leverentz

Author Affiliations +

Proceedings Volume 7258, Medical Imaging 2009: Physics of Medical Imaging; 72580G (2009) https://doi.org/10.1117/12.811374
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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.

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J. Eric Tkaczyk, David Langan, Xiaoye Wu, Daniel Xu, Thomas Benson, Jed D. Pack, Andrea Schmitz, Amy Hara, William Palicek, Paul Licato, and Jaynne Leverentz "Quantization of liver tissue in dual kVp computed tomography using linear discriminant analysis", Proc. SPIE 7258, Medical Imaging 2009: Physics of Medical Imaging, 72580G (10 March 2009); https://doi.org/10.1117/12.811374

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