title: CADA-computer-aided DaTSCAN analysis.
abstract: 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.
  
  
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Catanzaro, Italy. a.augimeri@unicz.it.
authors.forename: Antonio
authors.initials: A
authors.lastname: Augimeri
authors.affiliation: Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Catanzaro, Italy. a.cherubini@unicz.it.
authors.forename: Andrea
authors.initials: A
authors.lastname: Cherubini
authors.affiliation: Institute of Radiology, Nuclear Medicine Unit, University Magna Graecia, Catanzaro, Italy. cascini@unicz.it.
authors.forename: Giuseppe Lucio
authors.initials: GL
authors.lastname: Cascini
authors.affiliation: Institute of Radiology, Nuclear Medicine Unit, University Magna Graecia, Catanzaro, Italy. d.galea@unicz.it.
authors.forename: Domenico
authors.initials: D
authors.lastname: Galea
authors.affiliation: Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Catanzaro, Italy. me.caligiuri@unicz.it.
authors.forename: Maria Eugenia
authors.initials: ME
authors.lastname: Caligiuri
authors.affiliation: Institute of Neurology, University Magna Graecia, Catanzaro, Italy. g.barbagallo@unicz.it.
authors.forename: Gaetano
authors.initials: G
authors.lastname: Barbagallo
authors.affiliation: Institute of Neurology, University Magna Graecia, Catanzaro, Italy. g.arabia@unicz.it.
authors.forename: Gennarina
authors.initials: G
authors.lastname: Arabia
authors.affiliation: Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Catanzaro, Italy. quattrone@unicz.it.|Institute of Neurology, University Magna Graecia, Catanzaro, Italy. quattrone@unicz.it.
authors.forename: Aldo
authors.initials: A
authors.lastname: Quattrone
keywords: Computer-aided diagnosis; DaTSCAN; Parkinson’s disease