Automated volumes-of-interest identification for classical and atypical Parkinsonian syndrome differentiation using T2' MR imaging
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Automated volumes-of-interest identification for classical and atypical Parkinsonian syndrome differentiation using T2' MR imaging. / Forkert, N D; Schmidt-Richberg, A; Treszl, A; Hilgetag, C; Fiehler, J; Münchau, A; Handels, H; Boelmans, K.
In: METHOD INFORM MED, Vol. 52, No. 2, 01.01.2013, p. 128-36.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Automated volumes-of-interest identification for classical and atypical Parkinsonian syndrome differentiation using T2' MR imaging
AU - Forkert, N D
AU - Schmidt-Richberg, A
AU - Treszl, A
AU - Hilgetag, C
AU - Fiehler, J
AU - Münchau, A
AU - Handels, H
AU - Boelmans, K
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
KW - Adult
KW - Aged
KW - Diagnosis, Differential
KW - Germany
KW - Humans
KW - Image Interpretation, Computer-Assisted
KW - Magnetic Resonance Imaging
KW - Middle Aged
KW - Neuroimaging
KW - Parkinsonian Disorders
KW - ROC Curve
U2 - 10.3414/ME12-01-0044
DO - 10.3414/ME12-01-0044
M3 - SCORING: Journal article
C2 - 23450335
VL - 52
SP - 128
EP - 136
JO - METHOD INFORM MED
JF - METHOD INFORM MED
SN - 0026-1270
IS - 2
ER -