A mathematical observer study for the evaluation and optimization of compensation methods for myocardial SPECT using a phantom population that realistically models patient variability

The goal of this study was to develop and apply a population of phantoms that realistically models patient variability and use it to optimize and evaluate different compensation methods used during reconstruction process with respect to defect detection in myocardial SPECT images. Various combinations of attenuation, detector response and scatter compensation were used in this study. A major difference between this and previous studies was that the level of realism was significantly increased by inclusion of variability in heart and organ uptakes, in the heart size and orientation, and in the defect size and contrast. In this study we used a population of 24 4-D NCAT phantoms (half male, half female) recently developed with statistical models for organ uptake and organ size based on clinical data. Almost noise-free projection data of the torso, heart, liver, lungs, and other organs were simulated for each phantom using the SIMSET MC simulation code. They were then combined to form 72 sets of projections for each phantom using randomly sampled activity ratios from a clinically realistic distribution. Poisson noise was then added to the projection data. We applied the channelized hotelling observer (CHO) and receiver operating characteristic (ROC) analysis to optimize iteration number for OSEM and cutoff frequency of a 3-D post-reconstruction Butterworth filter. We found that the area under the ROC curve (AUC) values were reduced compared to a previous study that included significantly less phantom variability, even though the defect contrast was higher and noise level was lower. The resulting AUC values were similar to those obtained using patient data. We found, in agreement with the previous study, that including compensation for more effects resulted in improved defect detectability. However, the optimal filter cutoff frequency was increased compared to the previous study. These studies demonstrate the importance of including realistic levels of phantom variability in myocardial perfusion studies using simulated data.