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

TL;DR

This paper advances MCMC-based ideal observer computation methods to incorporate realistic anthropomorphic phantoms, enabling more accurate evaluation of image quality in clinical scenarios, demonstrated through myocardial perfusion SPECT imaging.

Contribution

The study introduces an improved MCMC approach that uses realistic anatomical databases for ideal observer calculations in medical imaging.

Findings

Anatomical database size influences ideal observer performance estimates.

Preliminary results show the impact of database size on IO computation.

Method enables more realistic assessment of image quality in clinical tasks.

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 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 to achieve 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 IO.