Ranking estimation methods in medical imaging without the use of a gold standard

Abstract

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 in order to know 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. Thus, researchers have often evaluated an estimation method by plotting its results against the results of another (more accepted) estimation method, which amounts to using one set of estimates as the pseudo-gold standard. In this dissertation, we present a maximum-likelihood approach for evaluating and comparing different estimation methods without the use of a gold standard with specific emphasis on the problem of evaluating EF estimation methods. We have named this method Regression Without Truth or RWT. Results of numerous simulation studies will be presented and indicate that the method can precisely and accurately estimate the parameters of a regression line without a gold standard, i.e., without the x-axis. We also characterize the performance of this method in comparison to conventional regression analysis using x-axis information. Also in this work we calculate the Fisher information for our method to quantify the performance of our evaluation method. Results of simulation studies are presented to show that we are very nearly efficient at estimating the parameters used in our method. In an attempt to further validate RWT we present the results of a volume estimation experiment using a physical phantom and two imaging systems (SPECT,CT). We conclude the dissertation with a discussion of the strengths and weaknesses of RWT. In an attempt to aid users of RWT we provide multiple consistency checks for users to evaluate results of RWT. Finally, we present some areas of potential application for RWT.