Detection performance theory for ultrasound imaging systems

Roger J. Zemp, Mark D. Parry, Craig K. Abbey, Michael F. Insana

Research output: Contribution to journalArticlepeer-review

Abstract

A rigorous statistical theory for characterizing the performance of medical ultrasound systems for lesion detection tasks is developed. A design strategy for optimizing ultrasound systems should be to adjust parameters for maximum information content, which is obtained by maximizing the ideal observer performance. Then, given the radio-frequency data, image and signal processing algorithms are designed to extract as much diagnostically relevant information as possible. In this paper, closed-form and low-contrast approximations of ideal observer performance are derived for signal known statistically detection tasks. The accuracy of the approximations are tested by comparing with Monte Carlo techniques. A metric borrowed and modified from photon imaging, Generalized Noise Equivalent Quanta, is shown to be a useful and measurable target-independent figure of merit when adapted for ultrasound systems. This theory provides the potential to optimize design tradeoffs for detection tasks. For example it may help us understand how to push the limits of specific features, such as spatial resolution, without significantly compromising overall detection performance.

Original languageEnglish (US)
Pages (from-to)300-310
Number of pages11
JournalIEEE transactions on medical imaging
Volume24
Issue number2
DOIs
StatePublished - Mar 2005

Keywords

  • Cancer
  • Decision theory
  • Image quality
  • Speckle

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics

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