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Proceedings Paper

Evaluating optimal CNR as a preset criteria for nonlinear moidal blending of dual energy CT data
Author(s): D. R. Holmes; A. Apel; J. G. Fletcher; L. S. Guimaraes; C. E. Eusemann; R. A. Robb
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Paper Abstract

Nonlinear blending of dual-energy CT data is available on current scanners. Selection of the blending parameters can be time-consuming and challenging. The purpose of this study was to determine if the Contrast-To-Noise Ratio (CNR) may be used ti automatic select of blending parameters. A Bovine liver was built with six syringes filled with varying concentrations of CT contrast yielding six 140kV HU levels (15, 47, 64, 79, 116, and 145). The phantom was scanned using 95 mAs @ 140kV and 404mAs @ 80 kV. The 80 and 140 kV datasets were blended using a modified sigmoid (moidal) function which requires two parameters - level and width. Every combination of moidal level and width was applied to the data, and the CNR was calculated as (mean(syringe ROI) - mean(liver ROI)) / STD(water). The maximum CNR was determined for each of the 6 HU levels. Pairs of blended images were presented in a blind manner to observers. Nine comparisons for each of the 6 HU settings were made by a staff radiologist, a resident, and a physicist. For each comparison, the observer selected the more "visually appealing" image. Outcomes from the study were compared using the Fisher Sign Test statistic. Analysis by observer showed a statistical (p<0.01) preference towards the optimal CNR image ranging from 71%-81%. Using moidal settings which provide the maximal CNR within the image is consistent with visually appealing images. Optimization of the viewing parameters of nonlinearly blended dual energy CT data may provide consistency across radiologists and facilitate the clinical review process.

Paper Details

Date Published: 16 March 2009
PDF: 6 pages
Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 726123 (16 March 2009); doi: 10.1117/12.813901
Show Author Affiliations
D. R. Holmes, Mayo Clinic (United States)
A. Apel, Siemens Healthcare (Germany)
J. G. Fletcher, Mayo Clinic (United States)
L. S. Guimaraes, Mayo Clinic (United States)
C. E. Eusemann, Siemens Healthcare (Germany)
R. A. Robb, Mayo Clinic (United States)


Published in SPIE Proceedings Vol. 7261:
Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling
Michael I. Miga; Kenneth H. Wong, Editor(s)

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