Utility of virtual monoenergetic images derived from a dual-layer detector-based spectral CT in the assessment of aortic anatomy and pathology: A retrospective case control study.

Journal Article (Journal Article)

OBJECTIVES: To evaluate the ability of the retrospectively generated virtual monoenergetic images (VMIs) from a dual-layer detector-based spectral computed tomography (SDCT) to augment aortic enhancement for the evaluation of aortic anatomy and pathology. METHODS: 98 patients with suboptimal aortic enhancement (≤200 HU) were retrospectively identified from SDCT scans. VMI from 40 to 80 keV were generated. Attenuation, noise, SNR, and CNR were measured at seven levels in the aorta. Image quality was graded on a 5-point scale, 5 being the best. From the VMI, an ideal set was chosen with mean vascular attenuation above 200 HU while maintaining diagnostic quality. Image parameters and quality of this ideal-set were compared to the standard 120-kVp images. RESULTS: The mean attenuation of all seven measured anatomical regions was 156.6 ± 61.7 HU in the 120-kVp images. Attenuation of the VMI from 40 to 70 keV were higher than the 120-kVp image, measuring 439.2 ± 215.3 HU, 298.5 ± 140.6 HU, 213.4 ± 94.3 HU, and 164.7 ± 90.2 HU, for 40 keV, 50 keV, 60 keV, and 70 keV, respectively (p value <0.01 for 40, 50, 60 keV; 0.07 for 70 keV). SNR and CNR showed similar trends. The 50 keV VMI had the best image quality (4.48 ± 0.84 vs. 2.24 ± 0.92 on 120-kVp images, p < 0.001). Attenuation, CNR, and SNR increased by 90.6%, 85.0%, and 108.1% at 50 keV compared to 120-kVp. CONCLUSIONS: A contrast-enhanced CT study can be optimized for the assessment of the aorta by using low-energy VMI obtained using SDCT. At the optimal monoenergetic level, attenuation, SNR, CNR and image quality were significantly higher than that of conventional polyenergetic images.

Full Text

Duke Authors

Cited Authors

  • Chalian, H; Kalisz, K; Rassouli, N; Dhanantwari, A; Rajiah, P

Published Date

  • 2018

Published In

Volume / Issue

  • 52 /

Start / End Page

  • 292 - 301

PubMed ID

  • 30212800

Electronic International Standard Serial Number (EISSN)

  • 1873-4499

Digital Object Identifier (DOI)

  • 10.1016/j.clinimag.2018.08.007


  • eng

Conference Location

  • United States