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Proceedings Paper

Prospective prediction and control of image properties in model-based material decomposition for spectral CT
Author(s): Wenying Wang; Matthew Tivnan; Grace J. Gang; J. Webster Stayman
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Paper Abstract

Model-based material decomposition (MBMD) directly estimates the material densities from the spectral CT data and has found opportunities for dose reduction via physical and statistical modeling and advanced regularization. However, image properties of material basis volumes can be complex. For example, spatial resolution, noise, and cross-talk can depend on acquisition parameters, regularization, patient size, and anatomical target. In this work, we propose a set of prospective prediction tools for the generalized local impulse response (LIR) that characterizes both in-basis spatial resolution and cross-basis response, as well as noise correlation. The accuracy of noise predictor was validated in a simulation study, comparing predicted and measured in- and cross-basis noise correlations. Employing these predictors, we composed a specialized regularization for cross-talk reduction and showed that such prediction tools are promising for task-based optimization in spectral CT applications.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113121Z (16 March 2020); doi: 10.1117/12.2549777
Show Author Affiliations
Wenying Wang, Johns Hopkins Univ. (United States)
Matthew Tivnan, Johns Hopkins Univ. (United States)
Grace J. Gang, Johns Hopkins Univ. (United States)
J. Webster Stayman, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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