Computation of the ensemble channelized hotelling observer signal-to-noise ratio for ordered-subset image reconstruction using noisy data

Edward J. Soares; Howard C. Gifford; Stephen J. Glick

doi:10.1117/12.480100

22 May 2003 Computation of the ensemble channelized Hotelling observer signal-to-noise ratio for ordered-subset image reconstruction using noisy data

Edward J. Soares, Howard C. Gifford, Stephen J. Glick

Author Affiliations +

Proceedings Volume 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment; (2003) https://doi.org/10.1117/12.480100
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

We investigated the estimation of the ensemble channelized Hotelling observer (CHO) signal-to-noise ratio (SNR) for ordered-subset (OS) image reconstruction using noisy projection data. Previously, we computed the ensemble CHO SNR using a method for approximating the channelized covariance of OS reconstruction, which requires knowledge of the noise-free projection data. Here, we use a “plug-in” approach, in which noisy data is used in place of the noise-free data in the aforementioned channelized covariance approximation. Additionally, we evaluated the use of smoothing of the noisy projections before use in the covariance approximation. Additionally, we evaluated the use of smoothing of the noisy projections before use in the covariance calculation. The task was detection of a 10% contrast Gaussian signal within a slice of the MCAT phantom. Simulated projections of the MCAT phantom were scaled and Poisson noise was added to create 100 noisy signal-absent data sets. Simulated projections of the scaled signal were then added to the noisy background projections to create 100 noisy signal-present data set. These noisy data sets were then used to generate 100 estimates of the ensemble CHO SNR for reconstructions at various iterates. For comparison purposes, the same calculation was repeated with the noise-free data. The results, reported as plots of the average CHO SNR generated in this fashion, along with 95% confidence intervals, demonstrate that this approach works very well, and would allow optimization of imaging systems and reconstruction methods using a more accurate object model (i.e., real patient data).

Citation Download Citation

Edward J. Soares, Howard C. Gifford, and Stephen J. Glick "Computation of the ensemble channelized Hotelling observer signal-to-noise ratio for ordered-subset image reconstruction using noisy data", Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); https://doi.org/10.1117/12.480100

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Signal to noise ratio

Signal detection

Smoothing

Solids

Image restoration

Data modeling

Statistical analysis

Show All Keywords

Keywords/Phrases

Search In:

Publication Years