Dopamine transporter (DaT) imaging (DaTSCAN) is useful for the differential diagnosis of parkinsonian syndromes. Visual evaluation of DaTSCAN images represents the generally accepted diagnostic method, but it is strongly dependent on the observer’s experience and shows inter- and intra-observer variability. A reliable and automatic method for DaTSCAN evaluation can provide objective quantification; it is desirable for longitudinal studies, and it allows for a better follow-up control. Moreover, it is crucial for an automated method to produce coherent measures related to the severity of motor symptoms. In this work, we propose a novel fully automated technique for DaTSCAN analysis that generates quantitative measures based on striatal intensity, shape, symmetry and extent. We tested these measures using a support vector machine (SVM) classifier. The proposed measures reached 100 % accuracy in distinguishing between patients with Parkinson’s disease (PD) and control subjects. We also demonstrate the existence of a linear relationship and an exponential trend between pooled structural and functional striatal characteristics and the Unified Parkinson’s disease Rating Scale (UPDRS) motor score. We present a novel, highly reproducible, user-independent technique for DaTSCAN analysis producing quantitative measures directly connected to striatum uptake and shape. In our method, no a priori assumption is required on the spatial conformation and localization of striatum, and both uptake and symmetry contribute to the index quantification. These measures can reliably support a computer-assisted decision system.

To explore the usefulness of histogram analysis of mean diffusivity (MD) derived from diffusion-weighted imaging of large infratentorial structures to distinguish parkinsonian syndromes. Local research ethics committee approval and informed consent were obtained. Ten patients with Parkinson disease (PD), nine with the parkinsonian variant of multiple system atrophy (MSA-P), seven with the cerebellar variant of MSA (MSA-C), 17 with progressive supranuclear palsy-Richardson syndrome (PSP-RS), and 10 healthy subjects were recruited. Histograms of MD values were generated for all pixels in the whole infratentorial compartment and separately for the whole brainstem, vermis, and cerebellar hemispheres. To assess the differences in MD values among groups, the Kruskal-Wallis test was used, followed by the Mann-Whitney U test for pairwise comparisons. All P values resulting from pairwise comparisons were corrected with the Bonferroni method. MSA-P and MSA-C groups had higher median MD values (P < .01) in the brainstem and cerebellum when compared with other groups; this finding was in line with the known consistent neurodegenerative damage in posterior cranial fossa structures in these diseases. Median MD values from cerebellar hemispheres were used to discriminate patients with MSA-C and those with MSA-P from patients with PD and those with PSP-RS (P < .01; sensitivity, specificity, and positive predictive value equaled 100%). Furthermore, patients with PSP-RS had significantly higher MD values in the vermis than did healthy subjects (P < .05) and patients with PD (P < .001). These findings support the clinical usefulness of diffusion imaging in the differential diagnosis of parkinsonism, suggesting that the minimally operator-dependent histogram analysis of the infratentorial structures and particularly of the whole cerebellar hemispheres can be used to distinguish patients with MSA-P and those with MSA-C from patients with PSP-RS and those with PD.