OBJECTIVES: 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. MATERIAL AND 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). CONCLUSION: The diagnostic role of automatically defined VOIs in differentiation of CPS and APS patients based on T2´ image sequences should be further investigated.
OBJECTIVES: 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 APSpatients. MATERIAL AND METHODS: 74 MRI datasets were available for this hypothesis generating trial, including image sequences from 24 healthy subjects, 33 CPS and 17 APSpatients. 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 APSpatients, 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). CONCLUSION: The diagnostic role of automatically defined VOIs in differentiation of CPS and APSpatients based on T2´ image sequences should be further investigated.
Authors: Yiming Xiao; Vladimir Fonov; Silvain Bériault; Fahd Al Subaie; M Mallar Chakravarty; Abbas F Sadikot; G Bruce Pike; D Louis Collins Journal: Int J Comput Assist Radiol Surg Date: 2014-05-20 Impact factor: 2.924