A comparison of cost functions for data-driven motion estimation in myocardial perfusion SPECT imaging

In myocardial perfusion SPECT imaging patient motion during acquisition causes severe artifacts in about 5% of studies. Motion estimation strategies commonly used are a) data-driven, where the motion may be determined by registration and checking consistency with the SPECT acquisition data, and b) external surrogate-based, where the motion is obtained from a dedicated motion-tracking system. In this paper a data-driven strategy similar to a 2D-3D registration scheme with multiple views is investigated, using a partially reconstructed heart for the 3D model. The partially-reconstructed heart has inaccuracies due to limited angle artifacts resulting from using only a part of the SPECT projections acquired while the patient maintained the same pose. The goal of this paper is to compare the performance of different cost-functions in quantifying consistency with the SPECT projection data in a registration-based scheme for motion estimation as the image-quality of the 3D model degrades. Six intensity-based metrics- Mean-squared difference (MSD), Mutual information (MI), Normalized Mutual information NMI), Pattern intensity (PI), normalized cross-correlation (NCC) and Entropy of the difference (EDI) were studied. Quantitative and qualitative analysis of the performance is reported using Monte-Carlo simulations of a realistic heart phantom including degradation factors such as attenuation, scatter and collimator blurring. Further the image quality of motion-corrected images using data-driven motion estimates was compared to that obtained using the external motion-tracking system in acquisitions of anthropomorphic phantoms and patient studies in a real clinical setting. Pattern intensity and Normalized Mutual Information cost functions were observed to have the best performance in terms of lowest average position error and stability with degradation of image quality of the partial reconstruction in simulations and anthropomorphic phantom acquisitions. In patient studies, Normalized Mutual Information based data-driven estimates yielded comparable image quality to that obtained using external motion tracking.