Secondary Logo

Journal Logo

Institutional members access full text with Ovid®

Improved Calcium Scoring at Dual-Energy Computed Tomography Angiography Using a High-Z Contrast Element and Novel Material Separation Technique

Lambert, Jack W., PhD; Sun, Yuxin, MSc; Ordovas, Karen G., MD; Gould, Robert G., ScD; Wang, Sizhe, MSc; Yeh, Benjamin M., MD

Journal of Computer Assisted Tomography: May/June 2018 - Volume 42 - Issue 3 - p 459–466
doi: 10.1097/RCT.0000000000000676
Thoracic and Cardiovascular Imaging
Buy

Objectives The aim of this study was to compare the accuracy of existing dual-energy computed tomography (CT) angiography coronary artery calcium scoring methods to those obtained using an experimental tungsten-based contrast material and a recently described contrast material extraction process (CMEP).

Methods Phantom coronary arteries of varied diameters, with different densities and arcs of simulated calcified plaque, were sequentially filled with water, iodine, and tungsten contrast materials and scanned within a thorax phantom at rapid-kVp-switching dual-energy CT. Calcium and contrast density images were obtained by material decomposition (MD) and CMEP. Relative calcium scoring errors among the 4 reconstructed datasets were compared with a ground truth, 120-kVp dataset.

Results Compared with the 120-kVp dataset, tungsten CMEP showed a significantly lower mean absolute error in calcium score (6.2%, P < 0.001) than iodine CMEP, tungsten MD, and iodine MD (9.9%, 15.7%, and 40.8%, respectively).

Conclusions Novel contrast elements and material separation techniques offer improved coronary artery calcium scoring accuracy and show potential to improve the use of dual-energy CT angiography in a clinical setting.

From the University of California, San Francisco, San Francisco, CA.

Received for publication April 19, 2017; accepted July 6, 2017.

Correspondence to: Jack W. Lambert, PhD, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA (e-mail: jack.lambert@ucsf.edu).

K.G.O. received salary support from General Electric. R.G.G. is a scientific advisor for AlgoMedica Inc and a stockholder in General Electric. B.M.Y. is an author with Royalties, Oxford University Press, and a shareholder in Nextrast Inc. The other authors declare no conflict of interest.

Research reported in this article was supported by the National Institutes of Health under the award numbers R01EB015476, 1R41DK104580, and UCSF-CTSI UL1 TR000004. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.