Abstract

RATIONALE AND OBJECTIVES:

Imaging and estimation of left ventricular function have major diagnostic and prognostic importance in patients with coronary artery disease. It is vital that the method used to estimate cardiac ejection fraction (EF) allows the observer to best perform this task. To measure task-based performance, one must clearly define the task in question, the observer performing the task, and the patient population being imaged. In this report, the task is to accurately and precisely measure cardiac EF, and the observers are human-assisted computer algorithms that analyze the images and estimate cardiac EF. It is very difficult to measure the performance of an observer by using clinical data because estimation tasks typically lack a gold standard. A solution to this "no-gold-standard" problem recently was proposed, called regression without truth (RWT).

MATERIALS AND METHODS:

Results of three different software packages used to analyze gated, cardiac, and nuclear medicine images, each of which uses a different algorithm to estimate a patient's cardiac EF, are compared. The three methods are the Emory method, Quantitative Gated Single-Photon Emission Computed Tomographic method, and the Wackers-Liu Circumferential Quantification method. The same set of images is used as input to each of the three algorithms. Data were analyzed from the three different algorithms by using RWT to determine which produces the best estimates of cardiac EF in terms of accuracy and precision.

RESULTS AND DISCUSSION:

In performing this study, three different consistency checks were developed to ensure that the RWT method is working properly. The Emory method of estimating EF slightly outperformed the other two methods. In addition, the RWT method passed all three consistency checks, garnering confidence in the method and its application to clinical data.