Application of pattern recognition framework for quantification of Parkinson's disease in DAT SPECT imaging

Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected parkinsonian syndromes. Visual classification or quantitative analysis of mean regional uptake has been performed in the past. Our objective is to enable further enhanced clinical utility in the diagnosis as well as tracking of progression in Parkinson's disease via quantification based on pattern recognition. We developed and implemented two such frameworks: first, we utilized shape/texture metrics that did require registration to a common structure/template; e.g. 3D moment-invariants, Haralick texture features, and multiple others. We also used a surface registration algorithm, which falls under the broad class of Large Deformation Diffeomorphic Metric Mapping (LDDMM). In this latter framework, we obtain a common coordinate system for the entire population based on MR images, and compare SPECT intensities across subjects in this common coordinate system. This method has the advantage of estimating population-based templates for each structure individually rather than using a predetermined collective atlas for all regions, as is customary. In this common coordinate system, we then used Principal Component Analysis (PCA) on intensities to obtain sub-regions (set of voxels inside the structure of interest) with highest variance in SPECT intensities across subjects. We show that the healthy and diseased populations can be subsequently distinguished. Via these methods, we also aimed to assess correlations with different clinical measures (e.g. UPDRS score, disease duration). In addition to enabling enhanced diagnostic task performance, these methods have considerable potential as biomarkers of PD progression.