Ideal-Observer Computation with anthropomorphic phantoms using Markov chain Monte Carlo

Abstract

In medical imaging, it is widely recognized that image quality should be objectively evaluated based on performance in clinical tasks. To evaluate performance in signal-detection tasks, the ideal observer (IO) is optimal but also challenging to compute in clinically realistic settings. Markov Chain Monte Carlo (MCMC)-based strategies have demonstrated the ability to compute the IO using pre-computed projections of an anatomical database. To evaluate image quality in clinically realistic scenarios, the observer performance should be measured for a realistic patient distribution. This implies that the anatomical database should also be derived from a realistic population. In this manuscript, we propose to advance the MCMC-based approach towards achieving these goals. We then use the proposed approach to study the effect of anatomical database size on IO computation for the task of detecting perfusion defects in simulated myocardial perfusion SPECT images. Our preliminary results provide evidence that the size of the anatomical database affects the computation of the IO.

Keywords: Image-quality evaluation; Markov chain Monte Carlo; SPECT.

Fig. 1.

(a) The estimate of IO test statistics as a function of total number iterations used for MCMC simulation. (b) The distribution of block estimate of log of IO test statistic.

Fig. 2.

(a) $〈\widehat{\Lambda }|H_0〉$ and (b) $〈\widehat{\Lambda }|H_1〉-var〈\widehat{\Lambda }|H_0〉$ as a function of number of image pairs used for observer study.

Fig. 3.

AUC values as a function of size of anatomical database. The error bar indicates 95% confidence interval.