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Proceedings Paper

Comparison of computational to human observer detection for evaluation of CT low dose iterative reconstruction
Author(s): Brendan Eck; Rachid Fahmi; Kevin M. Brown; Nilgoun Raihani; David L. Wilson
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Paper Abstract

Model observers were created and compared to human observers for the detection of low contrast targets in computed tomography (CT) images reconstructed with an advanced, knowledge-based, iterative image reconstruction method for low x-ray dose imaging. A 5-channel Laguerre-Gauss Hotelling Observer (CHO) was used with internal noise added to the decision variable (DV) and/or channel outputs (CO). Models were defined by parameters: (k1) DV-noise with standard deviation (std) proportional to DV std; (k2) DV-noise with constant std; (k3) CO-noise with constant std across channels; and (k4) CO-noise in each channel with std proportional to CO variance. Four-alternative forced choice (4AFC) human observer studies were performed on sub-images extracted from phantom images with and without a “pin” target. Model parameters were estimated using maximum likelihood comparison to human probability correct (PC) data. PC in human and all model observers increased with dose, contrast, and size, and was much higher for advanced iterative reconstruction (IMR) as compared to filtered back projection (FBP). Detection in IMR was better than FPB at 1/3 dose, suggesting significant dose savings. Model(k1,k2,k3,k4) gave the best overall fit to humans across independent variables (dose, size, contrast, and reconstruction) at fixed display window. However Model(k1) performed better when considering model complexity using the Akaike information criterion. Model(k1) fit the extraordinary detectability difference between IMR and FBP, despite the different noise quality. It is anticipated that the model observer will predict results from iterative reconstruction methods having similar noise characteristics, enabling rapid comparison of methods.

Paper Details

Date Published: 11 March 2014
PDF: 8 pages
Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370P (11 March 2014); doi: 10.1117/12.2043335
Show Author Affiliations
Brendan Eck, Case Western Reserve Univ. (United States)
Rachid Fahmi, Case Western Reserve Univ. (United States)
Kevin M. Brown, Philips Healthcare (United States)
Nilgoun Raihani, Philips Healthcare (United States)
David L. Wilson, Case Western Reserve Univ. (United States)

Published in SPIE Proceedings Vol. 9037:
Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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