Secondary Logo

Journal Logo

Institutional members access full text with Ovid®

Spectral Detector Computed Tomography Pulmonary Angiography

Improved Diagnostic Assessment and Automated Estimation of Window Settings Angiography of Pulmonary Arteries From Novel Spectral Detector Computed Tomography Provides Improved Image Quality if Settings are Adjusted

Große Hokamp, Nils, MD*†; Kessner, Rivka, MD*†; Van Hedent, Steven, MD*†‡; Graner, Frank Philipp, MSc*; Gupta, Amit, MD*†; Gilkeson, Robert, MD*†

Journal of Computer Assisted Tomography: November/December 2018 - Volume 42 - Issue 6 - p 850–857
doi: 10.1097/RCT.0000000000000743
Pulmonary and Thoracic Applications of Dual Energy CT
Buy

Objective This study aimed to evaluate image quality (IQ) of virtual monoenergetic images (VMIs) from novel spectral detector computed tomography angiography of the pulmonary arteries and to identify appropriate window settings for each kiloelectron volt level.

Materials Forty consecutive patients were included in this institutional review board–approved, Health Insurance Portability and Accountability Act–compliant study.

Signal- and contrast-to-noise ratios were calculated within the pulmonary trunk, and pulmonary/lobar/segmental arteries were calculated. The IQ and diagnostic certainty were rated by 2 radiologists on 5-point scales. In addition, they recorded appropriate window settings (center/width) that were linearly modeled against attenuation within the pulmonary trunk to generate generable results.

Results Signal- and contrast-to-noise ratios, IQ, and diagnostic certainty are significantly increased in low–kiloelectron volt VMIs (≤60 keV). Interrater agreement was excellent (ĸ = 0.89). We developed 2 linear models (R2: 0.91–0.97 and R2: 0.43–0.91, respectively, P ≤ 0.01), that suggest appropriate window settings.

Conclusions The VMIs from spectral detector computed tomography improve objective and subjective IQ in angiography of the pulmonary arteries, if window settings are adjusted; they can be automatically estimated using reported linear models.

From the *Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland;

Department of Radiology, Case Western Reserve University, Cleveland, OH; and

Department of Radiology, University Hospital Brussels, Brussels, Belgium.

Received for publication November 2, 2017; accepted January 16, 2018.

Correspondence to: Nils Große Hokamp, MD, Department of Radiology, University Hospitals Cleveland Medical Center, 11000 Euclid Ave, Cleveland, OH 44106 (e-mail: nils.hokamp@UHhospitals.org).

This study was supported in part by an Ohio Third Frontier Commission grant from the State of Ohio Department of Development and in part by a sponsored research agreement with Philips.

The authors declare no conflict of interest.

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