TY - JOUR
T1 - Detection performance theory for ultrasound imaging systems
AU - Zemp, Roger J.
AU - Parry, Mark D.
AU - Abbey, Craig K.
AU - Insana, Michael F.
N1 - Funding Information:
Manuscript received April 30, 2004; revised November 11, 2004. This work was supported in part by the National Institutes of Health (NIH) under Grant RO1 CA 82497. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was E. Krupinski. Asterisk indicates corresponding author. *R. J. Zemp was with the Department of Biomedical Engineering, University of California Davis, Davis, CA 95616 USA. He is now with the Optical Imaging Laboratory, Department of Biomedical Engineering, Texas A&M University, 233 Zachry Engineering Center, 3120 TAMU, College Station, TX 77843 USA (e-mail: [email protected]).
PY - 2005/3
Y1 - 2005/3
N2 - 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.
AB - 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.
KW - Cancer
KW - Decision theory
KW - Image quality
KW - Speckle
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U2 - 10.1109/TMI.2004.841226
DO - 10.1109/TMI.2004.841226
M3 - Article
C2 - 15754981
AN - SCOPUS:19044369433
SN - 0278-0062
VL - 24
SP - 300
EP - 310
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 2
ER -