Quantification of multiple mixed contrast and tissue compositions using photon-counting spectral computed tomography

Tyler E. Curtis; Ryan K. Roeder

doi:10.1117/1.JMI.6.1.013501

11 February 2019 Quantification of multiple mixed contrast and tissue compositions using photon-counting spectral computed tomography

Tyler E. Curtis, Ryan K. Roeder

Author Affiliations +

Journal of Medical Imaging, Vol. 6, Issue 1, 013501 (February 2019). https://doi.org/10.1117/1.JMI.6.1.013501

Abstract

Quantitative material decomposition of multiple mixed, or spatially coincident, contrast agent (gadolinium and iodine) and tissue (calcium and water) compositions is demonstrated using photon-counting spectral computed tomography (CT). Material decomposition is performed using constrained maximum likelihood estimation (MLE) in the image domain. MLE is calibrated by multiple linear regression of all pure material compositions, which exhibits a strong correlation (R² > 0.91) between the measured x-ray attenuation in each photon energy bin and known concentrations in the calibration phantom. Material decomposition of mixed compositions in the sample phantom provides color material concentration maps that clearly identify and differentiate each material. The measured area under the receiver operating characteristic curve is >0.95, indicating highly accurate material identification. Material decomposition also provides accurate quantitative estimates of material concentrations in mixed compositions with a root-mean-squared error <12 % of the maximum concentration for each material. Thus, photon-counting spectral CT enables quantitative molecular imaging of multiple spatially coincident contrast agent (gadolinium and iodine) and tissue (calcium and water) compositions, which is not possible with current clinical molecular imaging modalities, such as nuclear imaging and magnetic resonance imaging.

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Tyler E. Curtis and Ryan K. Roeder "Quantification of multiple mixed contrast and tissue compositions using photon-counting spectral computed tomography," Journal of Medical Imaging 6(1), 013501 (11 February 2019). https://doi.org/10.1117/1.JMI.6.1.013501

Received: 27 August 2018; Accepted: 22 January 2019; Published: 11 February 2019

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