TY - JOUR
T1 - Multi-template tensor-based morphometry: application to analysis of Alzheimer's disease
AU - Koikkalainen, Juha
AU - Lötjönen, Jyrki
AU - Thurfjell, Lennart
AU - Rueckert, Daniel
AU - Waldemar, Gunhild
AU - Soininen, Hilkka
AU - Alzheimer's Disease Neuroimaging Initiative, null
AU - Koikkalainen, Juha
AU - Lötjönen, Jyrki
AU - Thurfjell, Lennart
AU - Rueckert, Daniel
AU - Soininen, Hilkka
AU - Alzheimer's Disease Neuroimaging Initiative
N1 - Copyright © 2011 Elsevier Inc. All rights reserved.
PY - 2011/6/1
Y1 - 2011/6/1
N2 - In this paper methods for using multiple templates in tensor-based morphometry (TBM) are presented and compared to the conventional single-template approach. TBM analysis requires non-rigid registrations which are often subject to registration errors. When using multiple templates and, therefore, multiple registrations, it can be assumed that the registration errors are averaged and eventually compensated. Four different methods are proposed for multi-template TBM. The methods were evaluated using magnetic resonance (MR) images of healthy controls, patients with stable or progressive mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) from the ADNI database (N=772). The performance of TBM features in classifying images was evaluated both quantitatively and qualitatively. Classification results show that the multi-template methods are statistically significantly better than the single-template method. The overall classification accuracy was 86.0% for the classification of control and AD subjects, and 72.1% for the classification of stable and progressive MCI subjects. The statistical group-level difference maps produced using multi-template TBM were smoother, formed larger continuous regions, and had larger t-values than the maps obtained with single-template TBM.
AB - In this paper methods for using multiple templates in tensor-based morphometry (TBM) are presented and compared to the conventional single-template approach. TBM analysis requires non-rigid registrations which are often subject to registration errors. When using multiple templates and, therefore, multiple registrations, it can be assumed that the registration errors are averaged and eventually compensated. Four different methods are proposed for multi-template TBM. The methods were evaluated using magnetic resonance (MR) images of healthy controls, patients with stable or progressive mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) from the ADNI database (N=772). The performance of TBM features in classifying images was evaluated both quantitatively and qualitatively. Classification results show that the multi-template methods are statistically significantly better than the single-template method. The overall classification accuracy was 86.0% for the classification of control and AD subjects, and 72.1% for the classification of stable and progressive MCI subjects. The statistical group-level difference maps produced using multi-template TBM were smoother, formed larger continuous regions, and had larger t-values than the maps obtained with single-template TBM.
U2 - 10.1016/j.neuroimage.2011.03.029
DO - 10.1016/j.neuroimage.2011.03.029
M3 - Journal article
C2 - 21419228
SN - 1053-8119
VL - 56
SP - 1134
EP - 1144
JO - NeuroImage
JF - NeuroImage
IS - 3
ER -