Post on 08-Jul-2020
Surface Registration for Ventricular Morphometry
Analysis in Mild Cognitive ImpairmentJie Shi1, Cynthia M. Stonnington2, Paul M. Thompson3, Kewei Chen4, Boris Gutman3, Cole Reschke4,
Leslie C. Baxter5, Eric M. Reiman4, Richard J. Caselli6, and Yalin Wang1
1. CIDSE, Arizona State University 2. Department of Psychiatry and Psychology, Mayo Clinic Arizona 3. Imaging Genetics Center, Institute for Neuroimaging and Informatics,
University of Southern California 4. Banner Alzheimer’s Institute and Banner Good Samaritan PET Center 5. Human Brain Imaging Laboratory,
Barrow Neurological Institute 6. Department of Neurology, Mayo Clinic Arizona
jshi28@asu.edu
Introduction
We introduce a global conformal parameterization for the complex and
branching lateral ventricular surfaces with the hyperbolic Ricci flow
method. The parameterization minimizes angle distortions and has no
singularity points. From the conformal structure, we compute unique
and consistent geodesic curves on the ventricular surfaces. The
geodesic curves are then used as boundary conditions for ventricular
surface registration.
Methods
Fig. 1 summarizes the key steps in our ventricular surface registration
system.
Three boundaries are introduced on each ventricular surface as
shown in Fig. 1 (a). The process is called topology optimization.
Two paths connecting consistent endpoints of existing boundaries (as
show in Fig. 1 (b), points with the same colors) are traced on each
ventricular surface. The surface is then sliced open along the paths
and boundaries.
We compute hyperbolic metrics on ventricular surfaces after topology
optimization as shown in Fig. 1 (a) and embed them in the hyperbolic
space with the metrics and the simply connected surfaces as shown
in Fig. 1(b) with the hyperbolic Ricci flow method [1]. The hyperbolic
[3].
• Group comparison was performed in 71 mild cognitive impairment
(MCI) individuals who converted to incident AD in the subsequent 36
months, which we call the MCI converter group, and 62 MCI subjects
who did not convert to AD in the same period, which we call the MCI
stable group. All subjects are from the ADNI baseline dataset, as
summarized in Table 1.
Citations[1] Jin, M., et al., 2008. Discrete Surface Ricci Flow. IEEE Trans Vis
Comput Graph 14(5): 1030-1043.
[2] Chung, MK., et al., 2008. Tensor-Based Cortical Surface
Morphometry via Weighted Spherical Harmonic Representation. IEEE
Trans Med Imag 27, 1143-1151.
[3] Chung, MK., et al., 2005. Cortical thickness analysis in autism with
heat kernel smoothing. NeuroImage 25, 1256-1265.
[4] Chen, K., et al., 2011. Characterizing Alzheimer's disease using a
hypometabolic convergence index. Neuroimage 56, 52-60.
space is visualized with the Poincaré disk model as shown in Fig. 1
(c), which is the fundamental domain of a ventricular surface.
• Four fundamental domains at different periods are computed and
glued with the original fundamental domain to tile a finite portion of the
universal covering space, as shown in Fig. 1 (d).
• We recalculate the positions of the two paths in Fig. 1 (b) and their
complements as geodesics in the Poincaré disk, as shown in Fig. 1
(e). These geodesics are required to connect consistent endpoints of
existing geodesics, as shown in Fig. 1 (f).
We slice the universal covering space along the new geodesics and
form the canonical fundamental domain of the ventricular surface, as
shown in Fig. 1 (g).
The canonical fundamental domain is then converted a Euclidean
octagon with the Klein model, as shown in Fig. 1 (h). The octagon is
used as the parameter space for ventricular surface registration with
the constrained harmonic map.
• Permutation test with ventricular volume and surface area measures
did not detect significant differences between the two groups, the
permutation test corrected p-value is 0.0803 for the volume and
0.2922 for the surface area.
• Our method detected significant differences in the ventricular surface
shapes between the two groups. The permutation test corrected p-
value is 0.0172. Fig.2 shows the local areas with significant
differences on the ventricular surface.
• Correlation between ventricular morphology and the FDG-PET
derived brain functional biomarker, the hypometabolic convergence
index (HCI) [4] was performed to study the relationship between brain
structural morphology and functional changes in AD. Fig. 3 shows the
correlation p-map.
Figure 2. Statistical map showing local shape differences (p-values) between MCI
converter and MCI stable groups from the ADNI baseline dataset, based on tensor-
based morphometry (TBM) [2], which was smoothed by the heat kernel smoothing
method [3].
Figure 3. Correlation p-map between ventricular morphometry and HCI [4] with
smoothed TBM features [2,3].
Gender
(M/F)Education Age
MMSE at
Baseline
MCI
Converter
(n = 71)
45/26 15.99 ± 2.73 74.77 ± 6.81 26.83 ± 1.60
MCI Stable
(n = 62)44/18 15.87 ± 2.76 75.42 ± 7.83 27.66 ± 1.57
Table 1. Demographic information of studied MCI subjects in ADNI baseline dataset.
Experiments
• Surface deformations are measured by tensor-based morphometry
(TBM) [2], which is smoothed by the hear kernel smoothing method
Figure 1. System processing pipeline overview.