title: Automated volumes-of-interest identification for classical and atypical Parkinsonian syndrome differentiation using T2' MR imaging.
abstract: OBJECTIVE
  In clinical routine, patients with classical Parkinsonian syndromes (CPS) need to be differentiated from those with atypical Parkinsonian syndromes (APS), particularly with respect to prognosis and treatment decision. To date, this diagnosis is mainly based on clinical criteria, leading to failure rates up to 25%, motivating the development of image-based decision support systems. Magnetic resonance imaging (MRI) and in particular T2´ image sequences have been suggested as a potential marker for differential diagnosis. The aim of this study was to investigate whether automatically identified T2´ volumes-of-interest (VOIs) can be used for an automatic differentiation of CPS and APS patients.
  
  
  METHODS
  74 MRI datasets were available for this hypothesis generating trial, including image sequences from 24 healthy subjects, 33 CPS and 17 APS patients. First, a problem-specific reference atlas was generated using the healthy control datasets. Next, patients' datasets were registered to the atlas. Voxel-wise t-tests, reflecting significance levels of T2´ value differences between CPS and APS patients, were then applied for calculation of a p-map. Finally, the calculated p-map was thresholded and a connected component analysis was performed for final VOI detection. In parallel, manually defined VOIs were determined in grey and white matter for comparison.
  
  
  RESULTS
  Three VOIs in parts of the basal ganglia and the left occipital lobe were automatically identified by the presented method. There was a trend for higher area under the curve on multivariable receiver operating characteristic curves for automatically determined VOIs over manually defined VOIs (0.939 vs. 0.818, p = 0.0572).
  
  
  CONCLUSIONS
  The diagnostic role of automatically defined VOIs in differentiation of CPS and APS patients based on T2´ image sequences should be further investigated.
authors: Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. n.forkert@uke.uni-hamburg.de
authors: N D
authors: ND
authors: Forkert
authors: A
authors: A
authors: Schmidt-Richberg
authors: A
authors: A
authors: Treszl
authors: C
authors: C
authors: Hilgetag
authors: J
authors: J
authors: Fiehler
authors: A
authors: A
authors: Münchau
authors: H
authors: H
authors: Handels
authors: K
authors: K
authors: Boelmans
mesh_terms: D000328:Adult; D000368:Aged; D003937:Diagnosis, Differential; D005858:Germany; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D059906:Neuroimaging / Q000379:methods*; D020734:Parkinsonian Disorders / Q000175:diagnosis* / Q000473:pathology; D012372:ROC Curve