Generalized NEQ for assessment of ultrasound image quality
Author(s) -
Roger J. Zemp,
Craig K. Abbey,
Michael F. Insana
Publication year - 2003
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.480134
Subject(s) - computer science , observer (physics) , image quality , noise (video) , artificial intelligence , variance (accounting) , generalization , algorithm , computer vision , pattern recognition (psychology) , mathematics , image (mathematics) , physics , quantum mechanics , mathematical analysis , accounting , business
An information-theoretic framework for assessing and predicting ultrasound system performance for detection tasks is outlined. Current models of image quality for ultrasound detection tasks make some stringent assumptions, including large target area, that place limits on the applicability of the theory. New models of image quality for ultrasound systems are proposed based on the ideal observer that account for noise, system, and object properties. One result is an expression for the ideal observer detectability that is a generalization of Noise-Equivalent Quanta (NEQ), a measure used by photon imaging modalities. The detection signal-to-noise ratio is shown to be an integration of the generalized NEQ weighted by the spectral variance of the target signal (in contrast to the squared magnitude of the Fourier transform of the signal, as is the case with other modalities). This reflects that ultrasound systems are sensitive not to the magnitude of the medium parameters but rather the variance (spatial fluctuations) of these quantities. The resulting framework is amenable to measurement and prediction of system performance. The theory is used to predict the information content of the ultrasonic beam at various field points. New strategies are revealed for processing RF data that could improve detection of lesions.
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