Share Email Print

Proceedings Paper

Liver imaging: image quality evaluation and comparison between single and dual energy protocols
Author(s): Yuan Yao; Alec J. Megibow; Norbert J. Pelc
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Purpose: Some qualitative studies report a preference for blended dual-energy (DE) CT images over single energy (SE) images for liver CT imaging at the same dose. This is counter to theoretical expectations for simple tasks. We hypothesized that perhaps the broad spectrum of DE might be beneficial for a combination of tasks. We compare the CNR of SE and blended DE images for single and composite tasks, in part to see if they explain the preference. Methods: We simulated pre- and post-contrast SE abdominal CT imaging at various kVp but at constant average dose. Next, 80kVp and 140kVp scans with different dose allocations, dose matched to the SE images, were simulated. DE images were blended linearly with optimized blending ratios. The CNRs of liver against other soft tissues were used as a composite image quality metric for evaluation and comparison between the SE and DE protocols. In addition, the combination of the CNR of many tissue pairs pre- and post-contrast. Results: The CNR of pre-contrast single kVp imaging mostly increases with increasing tube voltage while 90kVp or lower energy yields higher CNR for post-contrast images, depending on the differential iodine concentration of each tissue. Similar trends are seen in the DE blended CNR curves. Results from the composite multi-CNR metric demonstrate that the SE protocol has better performance. Conclusions: Our study showed that an optimized SE protocol produces higher CNR, even for a range of tasks. This suggests that the reason for the radiologist preference must be something other than a fundamental advantage of DE.

Paper Details

Date Published: 19 March 2013
PDF: 9 pages
Proc. SPIE 8668, Medical Imaging 2013: Physics of Medical Imaging, 86681V (19 March 2013); doi: 10.1117/12.2008363
Show Author Affiliations
Yuan Yao, Stanford Univ. (United States)
Alec J. Megibow, NYU Langone Medical Ctr. (United States)
Norbert J. Pelc, Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 8668:
Medical Imaging 2013: Physics of Medical Imaging
Robert M. Nishikawa; Bruce R. Whiting; Christoph Hoeschen, Editor(s)

© SPIE. Terms of Use
Back to Top