Tensor-based morphometry with stationary velocity field diffeomorphic registration: application to ADNI

Abstract

Tensor-based morphometry (TBM) is an analysis technique where anatomical information is characterized by means of the spatial transformations mapping a customized template with the observed images. Therefore, accurate inter-subject non-rigid registration is an essential prerequisite for both template estimation and image warping. Subsequent statistical analysis on the spatial transformations is performed to highlight voxel-wise differences. Most of previous TBM studies did not explore the influence of the registration parameters, such as the parameters defining the deformation and the regularization models. In this work performance evaluation of TBM using stationary velocity field (SVF) diffeomorphic registration was performed in a subset of subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) study. A wide range of values of the registration parameters that define the transformation smoothness and the balance between image matching and regularization were explored in the evaluation. The proposed methodology provided brain atrophy maps with very detailed anatomical resolution and with a high significance level compared with results recently published on the same data set using a non-linear elastic registration method.

Fig. 1

Illustration of sagittal views of the unbiased template of the Nor group with different values of the registration parameters {α, σ}.

Fig. 2

STV plot of the Student’s t-statistic in the brain mask for different values of the registration parameters {α, σ}. For each curve, there are marks showing the t_p-threshold controlling FWE at level p = [0.05, 0.01, 0.005] (horizontal axis) as well as the number of voxels in the statistical map where t>t_p-threshold (vertical axis).

Fig. 3

One coronal and sagittal view of brain atrophy statistical maps of AD–Nor and MCI–Nor groups with different values of {α, σ}. Color-bar values denote Student’s t-statistic. Red/blue color denotes atrophy/expansion respectively. Note that different color map scales are used in AD–Nor and MCI–Nor comparisons. Vertical lines define slice locations.

Fig. 4

Illustration of corrected p-value (either FWE p or overall p) versus Student’s t-statistic (left) and uncorrected p-value (right) for several values of the registration parameters {α, σ}. FWE p-values when controlling multiple comparisons taking into account the complete set of parameters are also shown in the left panel (FWE all param).

Fig. 5

AD–Nor brain atrophy statistical map with registration parameter values {α = 5, σ = 2}. The white lines in the axial slice specify slice locations of the coronal views. Color-bar values denote Student’s t-statistic (and significance quantified as −log₁₀ p). Red/blue color denotes atrophy/expansion respectively.

Fig. 6

A sagittal view of the statistical maps of regression between log Jacobian values and the following variables: MMSE_baseline (top), MMSE_12month (middle) and age (bottom). Red/blue color denotes positive/negative values.

Fig. 7

Student’s t-statistic on volume difference between patient groups using different values of the registration parameters {α, σ}. The ROIs are left/right amygdala and hippocampus.

Fig. 8

STV curves of the Student’s t-statistic in the brain mask for different values of the registration parameters for diffeomorphic demons (left) and SVF diffeomorphic (right) registration methods. Only brain atrophy for the Nor–AD group comparison is shown. The star marks illustrate the t_p-threshold controlling FWE_0.05 for a few selected values of the parameters corresponding to the brain atrophy statistical maps shown in Figs. 3 and 9.

Fig. 9

One coronal and sagittal view of brain atrophy statistical maps of AD–Nor with different values of {s, g} in diffeomorphic demons. Color-bar values denote Student’s t-statistic. Red/blue color denotes atrophy/expansion respectively. Note that a different color map scale is used in the left column.