Parkinsonian syndromes (PS) are genetically and pathologically heterogeneous neurodegenerative disorders. Clinical distinction between different PS can be difficult, particularly in early disease stages. This paper describes an automatic method for the distinction between classical Parkinson's disease (PD) and progressive supranuclear palsy (PSP) using T2' atlases. This procedure is based on the assumption that regional brain iron content differs between PD and PSP, which can be selectively measured using T2' MR imaging. The proposed method was developed and validated based on 33 PD patients, 10 PSP patients, and 24 healthy controls. The first step of the proposed procedure comprises T2' atlas generation for each group using affine and following non-linear registration. For classification, a T2' dataset is registered to the atlases and compared to each one of them using the mean sum of squared differences metric. The dataset is assigned to the group for which the corresponding atlas yields the lowest value. The evaluation using leave-one-out validation revealed that the proposed method achieves a classification accuracy of 91%. The presented method might serve as the basis for an improved automatic classification of PS in the future.
Parkinsonian syndromes (PS) are genetically and pathologically heterogeneous neurodegenerative disorders. Clinical distinction between different PS can be difficult, particularly in early disease stages. This paper describes an automatic method for the distinction between classical Parkinson's disease (PD) and progressive supranuclear palsy (PSP) using T2' atlases. This procedure is based on the assumption that regional brain iron content differs between PD and PSP, which can be selectively measured using T2' MR imaging. The proposed method was developed and validated based on 33 PD patients, 10 PSP patients, and 24 healthy controls. The first step of the proposed procedure comprises T2' atlas generation for each group using affine and following non-linear registration. For classification, a T2' dataset is registered to the atlases and compared to each one of them using the mean sum of squared differences metric. The dataset is assigned to the group for which the corresponding atlas yields the lowest value. The evaluation using leave-one-out validation revealed that the proposed method achieves a classification accuracy of 91%. The presented method might serve as the basis for an improved automatic classification of PS in the future.