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Proceedings Paper

Quantitative image quality evaluation for cardiac CT reconstructions
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Paper Abstract

Maintaining image quality in the presence of motion is always desirable and challenging in clinical Cardiac CT imaging. Different image-reconstruction algorithms are available on current commercial CT systems that attempt to achieve this goal. It is widely accepted that image-quality assessment should be task-based and involve specific tasks, observers, and associated figures of merits. In this work, we developed an observer model that performed the task of estimating the percentage of plaque in a vessel from CT images. We compared task performance of Cardiac CT image data reconstructed using a conventional FBP reconstruction algorithm and the SnapShot Freeze (SSF) algorithm, each at default and optimal reconstruction cardiac phases. The purpose of this work is to design an approach for quantitative image-quality evaluation of temporal resolution for Cardiac CT systems. To simulate heart motion, a moving coronary type phantom synchronized with an ECG signal was used. Three different percentage plaques embedded in a 3 mm vessel phantom were imaged multiple times under motion free, 60 bpm, and 80 bpm heart rates. Static (motion free) images of this phantom were taken as reference images for image template generation. Independent ROIs from the 60 bpm and 80 bpm images were generated by vessel tracking. The observer performed estimation tasks using these ROIs. Ensemble mean square error (EMSE) was used as the figure of merit. Results suggest that the quality of SSF images is superior to the quality of FBP images in higher heart-rate scans.

Paper Details

Date Published: 24 March 2016
PDF: 10 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 978716 (24 March 2016); doi: 10.1117/12.2208341
Show Author Affiliations
Hsin-Wu Tseng, College of Optical Sciences, The Univ. of Arizona (United States)
GE Healthcare (United States)
Jiahua Fan, GE Healthcare (United States)
Matthew A. Kupinski, College of Optical Sciences, The Univ. of Arizona (United States)
William Balhorn, GE Healthcare (United States)
Darin R. Okerlund, GE Healthcare (United States)


Published in SPIE Proceedings Vol. 9787:
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Matthew A. Kupinski, Editor(s)

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