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Proceedings Paper

Combination of detection and estimation tasks using channelized scanning linear observer for CT imaging systems
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Paper Abstract

Maintaining or even improving image quality while lowering patient dose is always the desire in clinical CT imaging. Iterative reconstruction (IR) algorithms have been designed to help reduce dose and/or provide better image quality. In this work, the channelized scanning linear observer (CSLO) is applied to study the combination of detection and estimation task performance using CT image data. The purpose of this work is to design a task-­‐based approach to quantitatively evaluate image-­‐quality for different reconstruction algorithms. Low-­‐contrast objects embedded in head-­‐size and body-­‐size phantoms are imaged multiple times and reconstructed by FBP and an IR algorithm for this study. Independent signal present and absent ROIs cropped from images are channelized by Difference of Gauss channels for CSLO training and testing. Estimation receiver operating characteristic (EROC) curves and the area under EROC curve (EAUC) are calculated by CSLO as the figure of merit. The One-­‐ Shot method is used to compute the variance of the EAUC values. Results suggest that the IR algorithm studied in this work could efficiently reduce the dose approximately 54% to achieve an image quality comparable to conventional FBP reconstruction for the combined detection and estimation tasks.

Paper Details

Date Published: 17 March 2015
PDF: 10 pages
Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94160H (17 March 2015); doi: 10.1117/12.2081035
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)


Published in SPIE Proceedings Vol. 9416:
Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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