Optimizing imaging hardware for estimation tasks

Matthew A Kupinski, Eric W Clarkson, Kevin Gross, John W. Hoppin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Medical imaging is often performed for the purpose of estimating a clinically relevant parameter. For example, cardiologists are interested in the cardiac ejection fraction, the fraction of blood pumped out of the left ventricle at the end of each heart cycle. Even when the primary task of the imaging system is tumor detection, physicians frequently want to estimate parameters of the tumor, e.g. size and location. For signal-detection tasks, we advocate that the performance of an ideal observer be employed as the figure of merit for optimizing medical imaging hardware. We have examined the use of the minimum variance of the ideal, unbiased estimator as a figure of merit for hardware optimization. The minimum variance of the ideal, unbiased estimator can be calculated using the Fisher information matrix. To account for both image noise and object variability, we used a statistical method known as Markov-chain Monte Carlo. We employed a lumpy object model and simulated imaging systems to compute our figures of merit. We have demonstrated the use of this method in comparing imaging systems for estimation tasks.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsP. Chakraborty, E.A. Krupinski
Pages309-313
Number of pages5
Volume5034
DOIs
StatePublished - 2003
EventMedical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment - San Diego, CA, United States
Duration: Feb 18 2003Feb 20 2003

Other

OtherMedical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment
CountryUnited States
CitySan Diego, CA
Period2/18/032/20/03

Fingerprint

figure of merit
Imaging systems
hardware
Medical imaging
Hardware
Imaging techniques
estimators
Tumors
tumors
Fisher information matrix
Fisher information
physicians
signal detection
Markov chains
Signal detection
ejection
Markov processes
blood
Statistical methods
Blood

Keywords

  • Estimation tasks
  • Fisher information
  • Image quality

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Kupinski, M. A., Clarkson, E. W., Gross, K., & Hoppin, J. W. (2003). Optimizing imaging hardware for estimation tasks. In P. Chakraborty, & E. A. Krupinski (Eds.), Proceedings of SPIE - The International Society for Optical Engineering (Vol. 5034, pp. 309-313) https://doi.org/10.1117/12.480337

Optimizing imaging hardware for estimation tasks. / Kupinski, Matthew A; Clarkson, Eric W; Gross, Kevin; Hoppin, John W.

Proceedings of SPIE - The International Society for Optical Engineering. ed. / P. Chakraborty; E.A. Krupinski. Vol. 5034 2003. p. 309-313.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kupinski, MA, Clarkson, EW, Gross, K & Hoppin, JW 2003, Optimizing imaging hardware for estimation tasks. in P Chakraborty & EA Krupinski (eds), Proceedings of SPIE - The International Society for Optical Engineering. vol. 5034, pp. 309-313, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, San Diego, CA, United States, 2/18/03. https://doi.org/10.1117/12.480337
Kupinski MA, Clarkson EW, Gross K, Hoppin JW. Optimizing imaging hardware for estimation tasks. In Chakraborty P, Krupinski EA, editors, Proceedings of SPIE - The International Society for Optical Engineering. Vol. 5034. 2003. p. 309-313 https://doi.org/10.1117/12.480337
Kupinski, Matthew A ; Clarkson, Eric W ; Gross, Kevin ; Hoppin, John W. / Optimizing imaging hardware for estimation tasks. Proceedings of SPIE - The International Society for Optical Engineering. editor / P. Chakraborty ; E.A. Krupinski. Vol. 5034 2003. pp. 309-313
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