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

Article

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 like- lihood 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. © 2019 Society of Photo-Optical Instrumentation Engineers

Attributes

Attribute NameValues
Creator
  • Ryan Roeder

  • Tyler Curtis

Journal or Work Title
  • Journal of Medical Imaging

Volume
  • 6

Issue
  • 01

First Page
  • 013501-1

Last Page
  • 013501-7

ISSN
  • 2329-4310

Publication Date
  • 2019-01

Subject
  • MARS

  • Advanced Molecular Imaging System

Publisher
  • SPIE

Date Created
  • 2019-10-18

Language
  • English

Departments and Units
Record Visibility and Access Public
Content License
  • All rights reserved

Digital Object Identifier

doi:10.1117/1.jmi.6.1.013501

This DOI is the best way to cite this article.