Evaluating Estimation Techniques in Medical Imaging Without a Gold Standard: Experimental Validation.
Imaging is often used for the purpose of estimating the value of some parameter of interest. For example, a cardiologist may measure the ejection fraction (EF) of the heart to quantify how much blood is being pumped out of the heart on each stroke. In clinical practice, however, it is difficult to evaluate an estimation method because the gold standard is not known, e.g., a cardiologist does not know the true EF of a patient. An estimation method is typically evaluated by plotting its results against the results of another (more accepted) estimation method. This approach results in the use of one set of estimates as the pseudo-gold standard. We have developed a maximum-likelihood approach for comparing different estimation methods to the gold standard without the use of the gold standard. In previous works we have displayed the results of numerous simulation studies indicating the method can precisely and accurately estimate the parameters of a regression line without a gold standard, i.e., without the x-axis. In an attempt to further validate our method we have designed an experiment performing volume estimation using a physical phantom and two imaging systems (SPECT,CT).
Program in Applied Mathematics, The University of Arizona, Tucson, AZ.
John W
JW
Hoppin
Department of Radiology, The University of Arizona, Tucson, AZ ; Optical Sciences Center, The University of Arizona, Tucson, AZ.
Matthew A
MA
Kupinski
Department of Radiology, The University of Arizona, Tucson, AZ.
Donald W
DW
Wilson
Department of Radiology, The University of Arizona, Tucson, AZ.
Todd
T
Peterson
Optical Sciences Center, The University of Arizona, Tucson, AZ.
Benjamin
B
Gershman
Department of Radiology, The University of Arizona, Tucson, AZ.
George
G
Kastis
Program in Applied Mathematics, The University of Arizona, Tucson, AZ ; Department of Radiology, The University of Arizona, Tucson, AZ ; Optical Sciences Center, The University of Arizona, Tucson, AZ.
Eric
E
Clarkson
Department of Radiology, The University of Arizona, Tucson, AZ ; Optical Sciences Center, The University of Arizona, Tucson, AZ.
Lars
L
Furenlid
Program in Applied Mathematics, The University of Arizona, Tucson, AZ ; Department of Radiology, The University of Arizona, Tucson, AZ ; Optical Sciences Center, The University of Arizona, Tucson, AZ.
Harrison H
HH
Barrett
Regression analysis; image quality; parameter estimation