Supervised clustering approach to form functional images in positron emission tomography

The aim of this study is to investigate the availability of supervised statistical clustering algorithm for image-based model analysis in positron emission tomography or nuclear medicine to form a functional image. Voxel-by-voxel model analysis can derive functional images, but bad statistic property in voxel-based PET data and huge number of voxels prevent to realize practical algorithm to form parametric images. In this study, supervised clustering is applied to categorized PET data. The shape of tTAC is projected in multidimensional feature space, and noise propagation is modeled as multivariate Gaussian in the space. Simulation study shows that the estimates by the proposed algorithm was identical to the true values. And a clinical image of has physiologically acceptable aspect. We can conclude that supervised clustering sachem has potential to realize practical algorithm for voxel-based model analysis in PET.