Prospective prediction and control of image properties in model-based material decomposition for spectral CT

Wenying Wang; Matthew Tivnan; Grace J. Gang; J. Webster Stayman

doi:10.1117/12.2549777

16 March 2020 Prospective prediction and control of image properties in model-based material decomposition for spectral CT

Wenying Wang, Matthew Tivnan, Grace J. Gang, J. Webster Stayman

Author Affiliations +

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 113121Z (2020) https://doi.org/10.1117/12.2549777
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Wenying Wang, Matthew Tivnan, Grace J. Gang, and J. Webster Stayman "Prospective prediction and control of image properties in model-based material decomposition for spectral CT", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113121Z (16 March 2020); https://doi.org/10.1117/12.2549777

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