Multi-template tensor-based morphometry: application to analysis of Alzheimer's disease

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 -