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In medical imaging, it is widely recognized that image quality should beobjectively evaluated based on performance in clinical tasks. To evaluateperformance in signal-detection tasks, the ideal observer (IO) is optimal butalso challenging to compute in clinically realistic settings. Markov ChainMonte Carlo (MCMC)-based strategies have demonstrated the ability to computethe IO using pre-computed projections of an anatomical database. To evaluateimage quality in clinically realistic scenarios, the observer performanceshould be measured for realistic patient distribution. This implies that theanatomical database should also be derived from a realistic population. In thismanuscript, we propose to advance the MCMC-based approach to achieve thesegoals. We then use the proposed approach to study the effect of anatomicaldatabase size on IO computation for the task of detecting perfusion defects insimulated myocardial perfusion SPECT images. Our preliminary results provideevidence that the size of the anatomical database affects the computation ofIO.

Ideal-observer computation with anthropomorphic phantoms using Markov Chain Monte Carlo