Analysis of observer performance in unknown-location tasks for tomographic image reconstruction.
Our goal is to optimize regularized image reconstruction for emission tomography with respect to lesion detectability in the reconstructed images. We consider model observers whose decision variable is the maximum value of a local test statistic within a search area. Previous approaches have used simulations to evaluate the performance of such observers. We propose an alternative approach, where approximations of tail probabilities for the maximum of correlated Gaussian random fields facilitate analytical evaluation of detection performance. We illustrate how these approximations, which are reasonably accurate at low probability of false alarm operating points, can be used to optimize regularization with respect to lesion detectability.
HMS/MGH/MIT Martinos Center for Biomedical Imaging, 149 13th Street, Charlestown, Massachusetts 02129, USA. ayendiki@nmr.mgh.harvard.edu
Anastasia
A
Yendiki
Jeffrey A
JA
Fessler
D003661:Decision Support Techniques; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D016013:Likelihood Functions; D016015:Logistic Models; D009010:Monte Carlo Method; D016011:Normal Distribution; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D013269:Stochastic Processes; D014055:Tomography, Emission-Computed / Q000379:methods / Q000706:statistics & numerical data; D035501:Uncertainty