Comparison of the algebraic reconstruction technique with the maximum entropy reconstruction technique for a variety of detection tasks

Kyle J. Myers; Kenneth M. Hanson

doi:10.1117/12.18795

1 July 1990 Comparison of the algebraic reconstruction technique with the maximum entropy reconstruction technique for a variety of detection tasks

Kyle J. Myers, Kenneth M. Hanson

Author Affiliations +

Proceedings Volume 1231, Medical Imaging IV: Image Formation; (1990) https://doi.org/10.1117/12.18795
Event: Medical Imaging '90, 1990, Newport Beach, CA, United States

Abstract

A method for comparing reconstruction algorithms is presented based on the ability to perform certain detection tasks on the resulting images. The reconstruction algorithms compared are the algebraic reconstruction technique (ART) and the maximum entropy reconstruction method (MaxEnt). Task performance is assessed through a Monte Carlo simulation of the complete imaging process, including the generation of a set of object scenes, followed by data-taking, reconstruction, and performance of the specified task by a machine observer. For these detection tasks the figure of merit used for comparison is the detectability index, d'. When each algorithm is run with approximately optimized parameters, these studies find comparable values for d'.

Citation Download Citation

Kyle J. Myers and Kenneth M. Hanson "Comparison of the algebraic reconstruction technique with the maximum entropy reconstruction technique for a variety of detection tasks", Proc. SPIE 1231, Medical Imaging IV: Image Formation, (1 July 1990); https://doi.org/10.1117/12.18795

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