Region of interest evaluation of SPECT image reconstruction methods using a realistic brain phantom

A realistic numerical brain phantom, developed by Zubal et al, was used for a region-of-interest evaluation of the accuracy and noise variance of the following SPECT reconstruction methods: 1) Maximum-Likelihood reconstruction using the Expectation-Maximization (ML-EM) algorithm; 2) an EM algorithm using ordered-subsets (OS-EM); 3) a re-scaled block iterative EM algorithm (RBI-EM); and 4) a filtered backprojection algorithm that uses a combination of the Bellini method for attenuation compensation and an iterative spatial blurting correction method using the frequency-distance principle (FDP). The Zubal phantom was made from segmented MRI slices of the brain, so that neuro-anatomical structures are well defined and indexed. Small regions-of-interest (ROIs) from the white matter, grey matter in the center of the brain and grey matter from the peripheral area of the brain were selected for the evaluation. Photon attenuation and distance-dependent collimator blurring were modeled. Multiple independent noise realizations were generated for two different count levels. The simulation study showed that the ROI bias measured for the EM-based algorithms decreased as the iteration number increased, and that the OS-EM and RBI-EM algorithms (16 and 64 subsets were used) achieved the equivalent accuracy of the ML-EM algorithm at about the same noise variance, with much fewer number of iterations. The Bellini-FDP restoration algorithm converged fast and required less computation per iteration. The ML-EM algorithm had a slightly better ROI bias vs. variance trade-off than the other algorithms.