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Quantitative material characterization from multi‐energy photon counting CT
Author(s) -
Alessio Adam M.,
MacDonald Lawrence R.
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4790692
Subject(s) - imaging phantom , projection (relational algebra) , iterative reconstruction , materials science , biomedical engineering , energy (signal processing) , biological system , computer science , artificial intelligence , mathematics , optics , physics , statistics , algorithm , medicine , biology
Purpose: To quantify the concentration of soft‐tissue components of water, fat, and calcium through the decomposition of the x‐ray spectral signatures in multi‐energy CT images.Methods: Decomposition of dual‐energy and multi‐energy x‐ray data into basis materials can be performed in the projection domain, image domain, or during image reconstruction. In this work, the authors present methodology for the decomposition of multi‐energy x‐ray data in the image domain for the application of soft‐tissue characterization. To demonstrate proof‐of‐principle, the authors apply several previously proposed methods and a novel content‐aware method to multi‐energy images acquired with a prototype photon counting CT system. Data from phantom and ex vivo specimens are evaluated.Results: The number and type of materials in a region can be limited based on a priori knowledge or classification strategies. The proposed difference classifier successfully classified the image into air only, water+fat, water+fat+iodine, and water+calcium regions. Then, the content‐aware material decomposition based on weighted least‐square optimization generated quantitative maps of concentration. Bias in the estimation of the concentration of water and oil components in a phantom study was <0.10 ± 0.15 g/cc on average. Decomposition of ex vivo carotid endarterectomy specimens suggests the presence of water, lipid, and calcium deposits in the plaque walls.Conclusions: Initial application of the proposed methodology suggests that it can decompose multi‐energy CT images into quantitative maps of water, adipose, iodine, and calcium concentrations.