Clinically, cardiac SPECT is performed with static imaging protocols and visually assessed for perfusion defects based upon the relative intensity of myocardial regions. Dynamic imaging, however, has the potential to provide quantitative measures of flow, possibly improving diagnosis. The objective of this study was to compare the information content of dynamic and static thallium SPECT imaging as measures of myocardial perfusion. To make this comparison, canine studies were performed each with an occlusion placed on a coronary artery. Dynamic SPECT imaging was performed at rest and under adenosine stress, and subsets of the data were summed to provide corresponding 'static datasets for identical physiologic conditions. Microsphere-derived flow estimates were used as the gold standard. The dynamic data were fit to a two-compartment model to provide regional estimates of washin rate parameters. Occluded to normal ratios were also calculated for each study. Preliminary results show comparable correlations with microspheres for both washin and static scaled image intensities. In addition, dynamic data provided higher defect contrasts which were more accurate than the static occluded to normal ratios, using microspheres-derived flows as the standard. These results show promise for dynamic thallium imaging to provide improved information contrast compared to static imaging for myocardial perfusion SPECT studies.
Abstract--Dynamic cardiac SPECT imaging can provide quantitative and possibly even absolute measures of physiological parameters. However, a dynamic cardiac SPECT study involves a number of steps to obtain estimates of physiological parameters of interest. One of the key steps involves the selection of regions of interest. In the past, this has been done manually or by using a semi-automatic method. We propose to use cluster analysis to segment the data to obtain improved parameter estimates. The algorithm consists of using a standard k-means clustering followed by a blood input finetuning procedure using fuzzy k-means performed to obtain a more accurate blood input function. Computer simulations were used to test the algorithm and to compute bias in kinetic rate parameters with and without the use of blood input fine-tuning. This was followed by performing eight studies in three canines and three studies in two patients with a dynamic cardiac perfusion SPECT protocol. The short-axis slice image data were used as input for the cluster analysis program as well as for a previously validated semi-automatic method. All of the time activity curves were fit to a two-compartment model. Parametric images of the wash-in rate parameter were obtained after cluster analysis. The wash-in rate estimates from the selected regions of interest with both of the methods were compared using microsphere derived flows as a gold standard in the case of canine studies. Our results suggest that in regions with low noise, cluster analysis provides parameter estimates comparable to the semi-automatic method in addition to providing improved visual defect localization and contrast. Moreover, the clustered curves have less noise and yield reasonable fits where with the semi-automatic method the fitting routine sometimes failed to converge. The use of clustering also required less manual intervention than the semi-automatic method. These results indicate that use of clustering may bring dynamic cardiac SPECT closer to clinical feasibility.
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