Thursday, October 15 | 4:00 PM - 6:00 PM
It has been advocated to use objective measures of image quality (IQ) for assessing and optimizing medical imaging systems. Objective measures of IQ quantify the performance of an observer at a specific diagnostic task. Binary signal detection tasks and joint signal detection and localization (detection-localization) tasks are commonly considered in medical imaging. When optimizing imaging systems for binary signal detection tasks, the performance of the Bayesian Ideal Observer (IO) has been advocated for use as a figure-of-merit. The IO maximizes the observer performance that is summarized by the receiver operating characteristic (ROC) curve. When signal detection-localization tasks are considered, the IO that implements a modified generalized likelihood ratio test (MGLRT) maximizes the observer performance as measured by the localization ROC (LROC) curve. However, computation of the IO test statistic generally is analytically intractable. To address this difficulty, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been proposed. However, current applications of MCMC methods have been limited to relatively simple stochastic object models (SOMs). When the IO is difficult or intractable to compute, the optimal linear observer, known as the Hotelling Observer (HO), can be employed to evaluate objective measures of IQ. However, computation of the HO can also be challenging or even intractable because a potentially large covariance matrix needs to be estimated and subsequently inverted. In this dissertation, we introduce supervised learning-based methods for approximating the IO and the HO for binary signal detection tasks. A supervised learning method to approximate the IO for signal detection-localization tasks is also presented. In addition, we propose a novel deep learning method named progressively growing AmbientGAN (ProAmGAN) for establishing realistic SOMs from imaging measurements, which further enables the assessment of objective measures of IQ. Finally, a novel sampling-based method named MCMC-GAN is developed to approximate the IO. This method can be implemented with realistic object models and therefore extends the domain of applicability of the MCMC techniques.
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