Dynamical Cluster Analysis for the Detection of Microglia Activation

Abstract

Dynamical cluster analysis (DCA) was used to extract sets of representative time courses to detect brain lesions using positron emmision tomography (PET) data. DCA is an adaptive hard-clustering algorithm where the number of clusters k is not initially fixed but is dynamically changed by generation and fusion of clusters during runtime. We analyzed PET data sets of 9 patients applying DCA repeatedly. We compared the results that vary in the number of clusters even on the same data set. As validation measure we used the mean square quantization error (MSQE). We found that the MSQE was strictly correlated with k only on 4 of the 9 data sets. We propose DCA for extracting the reference time course required in reference tissue modeling [7]. In the case of one patient, we checked the ability of DCA to characterize directly the three most interesting regions, reference tissue, the veins and the lesion and how this ability relates to high validation scores. The characterisation of all three regions was not reproducible in all of the runs, however, runs rated high in validity by the MSQE were able to reproduce all the three regions.