Markov chain Monte Carlo (MCMC) based ideal observer estimation using a parameterized phantom and a pre-calculated dataset

Abstract

The ideal observer (IO) employs complete knowledge of the available data statistics and sets an upper limit on the observer performance on a binary classification task. Kupinski proposed an IO estimation method using Markov chain Monte Carlo (MCMC) techniques. In principle, this method can be generalized to any parameterized phantoms and simulated imaging systems. In practice, however, it can be computationally burdensome, because it requires sampling the object distribution and simulating the imaging process a large number of times during the MCMC estimation process. In this work we propose methods that allow application of MCMC techniques to cardiac SPECT imaging IO estimation using a parameterized torso phantom and an accurate analytical projection algorithm that models the SPECT image formation process. To accelerate the imaging simulation process and thus enable the MCMC IO estimation, we used a phantom model with discretized anatomical parameters and continuous uptake parameters. The imaging process simulation was modeled by pre-computing projections for each organ in the finite number of discretely-parameterized anatomic models and taking linear combinations of the organ projections based on sampling of the continuous organ uptake parameters. The proposed method greatly reduces the computational burden and makes MCMC IO estimation for cardiac SPECT imaging possible.