An approach to evaluate myocardial perfusion defect assessment for projection-based DECT: A phantom study. Academic Article uri icon

Overview

abstract

  • INTRODUCTION: Dual-energy CT (DECT) can improve the accuracy of myocardial perfusion CT with projection-based monochromatic (DECT-MCE) and quantification of myocardial iodine in material decomposition (DECT-MD) reconstructions. However, evaluation of multiple reconstructions is laborious and the optimal reconstruction to detect myocardial perfusion defects is unknown. METHODS: Left ventricular (LV) phantoms with artificial perfusion defects were scanned using DECT and single energy cardiac computed tomography angiography (SECT). Reconstructions of DECT-MCE at 40, 70, 100 and 140 keV, DECT-MD pairs of water, iodine, iron and fat, and SECT were evaluated using a 17-segment myocardial model. The diagnostic performance of each reconstruction was calculated on a per-segment basis and compared across DECT reconstructions. RESULTS: Over 34 phantoms with artificial perfusion defects were found in 64/578 (11%) of segments, the sensitivity of DECT-MCE at 40, 70, 100, and 140 keV was 100% (95% confidence interval (CI): 93-100), 100% (95% CI: 93-100), 71% (95% CI: 56-83), and 25% (95% CI: 14-40), respectively, with a significant decline between 70 keV and 100 keV (p < 0.001). The specificity of DECT-MCE was 100% at all energies (95% CI: 99-100). As a group, the DECT-MD iodine background reconstructions had significantly lower sensitivity than the remaining modes (2.1% [95% CI, 0.05-11.1], vs. 100% [95% CI, 92.6-100], p < 0.001). Specificity of all material pair modes remained 100%. CONCLUSIONS: Using LV phantom models, the approach with the best sensitivity and specificity to assess myocardial perfusion defects with DECT are reconstructions of DECT-MCE at 40 or 70 KeV and DECT-MD without iodine background.

publication date

  • January 28, 2020

Research

keywords

  • Myocardial Perfusion Imaging

Identity

Scopus Document Identifier

  • 85080069102

Digital Object Identifier (DOI)

  • 10.1016/j.clinimag.2019.09.016

PubMed ID

  • 32120307

Additional Document Info

volume

  • 63