CADA-computer-aided DaTSCAN analysis

Methods: 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.

Background: 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.