Comparison of computational to human observer detection for evaluation of CT low dose iterative reconstruction

Brendan Eck; Rachid Fahmi; Kevin M. Brown; Nilgoun Raihani; David L. Wilson

doi:10.1117/12.2043335

11 March 2014 Comparison of computational to human observer detection for evaluation of CT low dose iterative reconstruction

Brendan Eck, Rachid Fahmi, Kevin M. Brown, Nilgoun Raihani, David L. Wilson

Author Affiliations +

Proceedings Volume 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment; 90370P (2014) https://doi.org/10.1117/12.2043335
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

Brendan Eck, Rachid Fahmi, Kevin M. Brown, Nilgoun Raihani, and David L. Wilson "Comparison of computational to human observer detection for evaluation of CT low dose iterative reconstruction", Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370P (11 March 2014); https://doi.org/10.1117/12.2043335

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available