excursions in epilepsy and Alzheimer`s dementia

A thesis proposal submitted to the Department of Biomedical Engineering
From voxels to volumes: excursions in
epilepsy and Alzheimer’s dementia
Simon Duchesne, ing., M.Sc.
December 2004
Table of contents
ABSTRACT
4
1. INTRODUCTION AND MOTIVATION
4
2. SUMMARY OF CONTRIBUTIONS
5
3. BACKGROUND
5
3.1 Areas of application
5
3.1.1 Temporal lobe epilepsy
Incidence
Etiology
Therapy
Relevance of MR imaging
Outstanding issues
5
5
5
5
6
6
3.1.2 Alzheimer’s dementia
Incidence / Prevalence
Etiology
Therapy
Relevance of MR Imaging
Outstanding issues
6
6
6
6
7
7
3.2 Registration
7
3.3 Existing methods
8
3.3.1 Voxel-based morphometry
Technique
Application
8
8
9
3.3.2 Area-based morphometry
Technique
Application
10
10
10
3.3.3 Structure-based morphometry
Technique
Applications
10
10
11
3.3.4 Volume-based morphometry
Technique
12
12
4. HYPOTHESIS AND GOAL
12
5. NOVEL METHODOLOGIES
13
5.1 Subjects – Epilepsy study
13
2
5.2 Subjects – Alzheimer’s dementia study
13
5.3 Novel methodology for the prediction of therapeutic effect
14
5.4 Novel methodology for improved atlas definition and structure segmentation
14
5.5 Novel methodology for the classification of neurological diseases
15
5.6 Novel methodology for the prediction of clinical evaluation scores from MR scans
15
6. EXPERIMENTAL PLAN AND RESULTS
6.1 Experiment 1 – VBM of TLE HA subjects
6.2 Experiment 2 – VBM of TLE NV subjects
6.3 Experiment 3 – TLE surgical outcome prediction
6.4 Experiment 4 – Validation of automated structure segmentation in NC
6.5 Experiment 5 – Lateralization of seizure focus in TLE
6.6 Experiment 6 – Classification of subjects into normal aging, MCI and AD
6.7 Experiment 7 – Prediction of cognitive decline in MCI
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16
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19
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7. CONTRIBUTIONS
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7.1 Novel methodologies
7.2 Novel applications
7.3 Publications to date
20
20
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8. CONCLUSION
21
9. BIBLIOGRAPHY
21
10. FIGURES
26
3
Abstract
Anatomical magnetic resonance imaging can assist in accurate and early diagnosis of
many neurological diseases as well as identify possible targets for drug therapy.
Structure-based morphometry has been the most commonly used method to identify
brain areas that are the site of seizure focus in temporal lobe epilepsy (TLE) or
associated with decline Mild Cognitive Impairment (MCI) to Alzheimer’s dementia (AD),
as volume and atrophy are taken as surrogate measures of the state of these diseases.
Such analyses however are performed via manual segmentation, a time-consuming
approach prone to subjectivity. This thesis proposes novel, automated and objective
methodologies in area-based morphometry, volume-based morphometry, iterative atlas
creation and prediction of clinical variables, for the study of diseases such as TLE and
AD. This work also presents novel applications of these and existing methods, in grey
and white matter analysis of TLE, MCI and AD patients, prediction of TLE surgical
outcome, validation of automated segmentation in normal controls, lateralization of
seizure focus in TLE patients with or without ipsilateral hippocampal atrophy,
differentiation of AD and MCI from normal aging, prediction of MCI conversion to AD
and finally prediction of cognitive decline in MCI. The proposed methods will facilitate
the understanding of TLE, therapy follow-up and pre-surgical evalution and planning.
They will also improve our ability to detect AD early and to differentiate it from normal
aging or MCI.
1. Introduction and motivation
The socio-economic impact of neurological diseases and disorders is increasing and
poses a burden on institutional and natural caregivers that must be addressed by
additional research.
Neuroimaging, broadly separated into structural (e.g. Computed Tomography (CT),
anatomical magnetic resonance imaging (aMRI) and diffusion tensor MR (DTI)) and
functional modalities (e.g. positron emission tomography (PET), single-photon emission
CT (SPECT) and functional MRI (fMRI)) enable us to visualize pathologically induced
brain changes in the living subject, and use those as surrogate measures of the state of
the disease. These technologies can help in achieving an earlier diagnosis, characterize
disease progression and generally increase therapy delivery and efficacy.
This thesis will be set in the growing field of quantitative analyses of medical images of
the human brain. In ideal conditions, it remains a challenging task for computer vision.
The methods proposed are meant to be applicable for a wide range of neurological
diseases, as well as being applicable in a clinical setting; they must therefore be
reproducible, practical, objective and sufficiently sensitive to detect changes due to the
pathology of interest.
The layout of this work will be two-fold: on the one hand, to describe current and novel
methodologies, and on the other to demonstrate their applicability and versatility in two
specific diseases: temporal lobe epilepsy (TLE) and Alzheimer’s dementia (AD).
4
2. Summary of contributions
A.
B.
C.
D.
A novel MRI area-based morphometry methodology;
A novel MRI volume-based morphometry methodology;
A novel iterative for iterative atlas creation;
A novel methodology for the prediction of clinical evaluation scores from MR
data;
E. Novel applications of existing and proposed methodologies in temporal lobe
epilepsy and Alzheimer’s dementia research.
3. Background
3.1 Areas of application
3.1.1 Temporal lobe epilepsy
Incidence
Epilepsy affects up to 3% of Canadians in their lifetime [1]. Around 20% of postchildhood recurring cases are pharmacoresistant or intractable. Furthermore, in three
quarters of the latter cases, the seizure focus, or location of primary discharge, is
localized within the medial temporal lobe (MTL) [1]; hence the appellation temporal lobe
epilepsy (TLE) (see Figure 1)
Etiology
The most commonly described pathological findings in TLE are neuronal loss and
gliosis of the hippocampus and the parahippocampal region [2] that have been shown
to correlate with hippocampal atrophy (HA) on volumetric MRI [3] [4]. Unilateral HA is
present in around 80% of cases where the seizure focus is located in the medial
temporal lobe (see Figure 2)[5] and thus when present is highly predictive of ipsilateral
seizure focus location. In the MNI database 5% of patients exhibit bilateral HA; the
remaining group (normal volumes or NV) shows no significant difference in hippocampi
volumes when compared to a matched normal control population [6]. For these latter
cases HC volumetry is therefore unable to provide seizure focus lateralization.
Therapy
TLE patients resistant to anticonvulsant drugs can be helped by surgery, providing the
seizure focus is properly identified and surgically removed. The standard procedure
consists in hippocampal and amygdala resection [7]. Post-surgical outcome has been
characterized according to seizure frequency and other factors; for our purposes, we
will binarize outcome as being either positive (complete remission and disappearance of
all seizures) or negative (all other levels of complications). While the majority of patients
undergoing surgery have positive outcome, it is difficult a priori to determine with
accuracy if the procedure will be successful [8]. Further work is necessary to improve
the surgical approach, increase its chance of success and translate those advances into
higher well-being for the patient and reduced costs for the health care system.
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Relevance of MR imaging
The advent of high resolution MRI has had a major impact on the presurgical
evaluation, as HC volumetry can lateralize the seizure focus in the majority of cases
while anatomical images guide the surgical path planning process. It is also common to
proceed with surgically implanted EEG electrodes within the MTL to precisely determine
the location of the seizure focus. Reducing the surgical evaluation’s reliance on this
procedure would be a tremendous step forward in reducing per-case human and
financial costs. The goal is to position automated anatomical MRI analysis as a noninvasive and accurate method for lateralization of seizure focus (see Figure 3).
Outstanding issues
This thesis will attempt to address the following outstanding issues in the study of TLE:
o Characterize the spatio-temporal distribution of anatomical changes which are
related to TLE (HA) and TLE (NV);
o Determine if these differences can be used to lateralize the seizure focus ; and
o Determine if these differences can be used to predict surgical outcome.
3.1.2 Alzheimer’s dementia
Incidence / Prevalence
Alzheimer’s dementia (AD) is the most common cause of dementia in the elderly (65
years and older), responsible for 75% of all dementia cases [9, 10]. The progression of
AD is gradual, and the average patient lives 8 to 10 years after onset of symptoms [9].
The lower bound on its prevalence in the general Canadian population has been
estimated at 250,000 people, with an incidence of 60,000 new cases each year [10]
(see Figure 4). As the Canadian population ages, the prevalence of AD is expected to
triple over the next 50 years [11]; improving care while reducing the socio-economic
impact of AD is therefore and important and necessary topic of research.
Etiology
AD is a progressive neurodegenerative disorder associated with disruption of neuronal
function and gradual deterioration in cognition, function, and behavior [12] . The etiology
is generally believed to be neuronal loss due to accumulation of abnormal proteins into
neurofibrillary tangles and extra-cellular plaques (see Figure 5). The first case of the
disease was reported in 1907 by Dr Alois Alzheimer [13]; interestingly, it was
reconfirmed in 1998 [14] when the original histopathological slices of Mrs Auguste D.
were miraculously recovered in Munich [15].
Therapy
There are no known cure for AD. Therapeutic attempts so far are aimed at slowing
down the degenerative process, with the hope of stopping it entirely however, results
from clinical trials show mitigated evidence that this is happening. Current consensus
statements have emphasized the need for early detection. The diagnosis of clinically
probable AD can be made with high accuracy in living subjects only once the stage of
dementia has been reached. It requires clinical, neuropsychological and imaging
assessments [9] and is based on a number of criteria as defined by the National
6
Institute of Neurological and Communicative Disorders and Stroke (NINCDS) and the
Alzheimer's Disease and Related Disorders Association (ADRDA) [16]. It can only be
confirmed by postmortem histopathology. Diagnosis is thus time-consuming and, more
importantly, arrives too late for early treatment to be effective at delaying the onset of
debilitating symptoms. The invention of a robust method for early detection of AD would
represent a significant breakthrough, as it would allow any future treatment to slow or
perhaps even stop the degenerative process before dementia develops. The difficulties
in early clinical detection lie for the most part in the similarities between cognitive
impairment due to normal aging processes and the initial manifestations of AD [17],
including mild cognitive impairment (MCI), different from normal aging-associated
senility but strongly associated to decline into AD (see Figure 6).
Relevance of MR Imaging
Neuropathological studies have shown that brain degeneration occurs very early in the
course of the disease, even before the first clinical signs, in certain regions such as the
MTL[9](Figure 5). For patients with an increased risk of developing AD (e.g. MCI) a
number of approaches can be considered in order to achieve an early diagnosis, all with
varying degrees of success (see Figure 7). Screening neuropsychological tests are
necessary to recognize and monitor these at-risk subjects, but there is no perfectly
accurate cognitive marker of early AD identified to date [18]. Neuroimaging approaches
must be considered, however disadvantages of techniques other than routine aMRI
preclude them from being effective, widespread early AD detection mechanisms. PET
[19] and SPECT [20, 21] offer value in the differential diagnosis of AD from other cortical
and subcortical dementias and may also offer prognostic value. However, both
modalities are minimally invasive procedures with radiation dose limitations and
therefore cannot be repeatedly performed on a single patient nor used as a screening
mechanism for large populations. fMRI [22] requires much technical expertise and must
rely cognitive testing which, as previously mentioned, has not been shown to be
sufficiently sensitive or specific. Other techniques such as MR spectroscopy [23], MR
diffusion tensor [24] and MR magnetization transfer [25] show promise for the future, but
they are difficult to implement in a clinical setting without a dedicated research group for
technical support. Finally, as compared to aMRI, CT images lack the detailed soft-tissue
information necessary for detecting subtle structure changes associated with the early
disease. A technique based on aMRI that would achieve the required sensitivity and
specificity would stand to benefit patients and health care systems immediately.
Outstanding issues
This thesis will attempt to address the following outstanding issues in the study of AD:
o Characterize the spatio-temporal distribution of anatomical changes which are
related to AD, MCI and normal aging in the brain;
o Determine if these differences can be used to differentiate normal aging, MCI and
AD; and
o Determine if these differences predict cognitive decline and/or progression
3.2 Registration
Before discussing existing methods in processing of MR images, the concept of
registration must be discussed. It is a process where individual subject images are
aligned into a reference space, allowing spatial comparisons to be made between
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cohorts. It therefore forms a common starting point for all imaging techniques that will
be discussed in the next section.
The registration process is typically broken down in a two phase process identifying
linear and non-lin ear components required to align datasets. The linear transformation
is used to correct global differences in brain size, orientation and shape. In the nonlinear registration phase, a dense deformation field is estimated, matching image
features (labels or intensities) from a source volume to those of a given target at a local
level, typically in a hierarchical fashion, with the aim of reducing a specific cost function.
This deformation field then embeds unique information about the individual brain under
study. Many registration processes exist, however as registration is not the topic of this
thesis, the reader is referred to review articles on the subject [26-28].
In this thesis, the registration algorithm that will be used is ANIMAL, developed by
Collins et al.[29] and in routine usage at the Montreal Neurological Institute and Brain
Imaging Center (MNI BIC). In particular, this algorithm attempts to match image
gradients of grey-level intensity at a local level in successive blurring steps, by
minimizing cross-correlation between the source and target images.
3.3 Existing methods
The following paragraphs will attempt to capture essential literature written on methods
used to compare and classify anatomical images of the brain, along with examples of
applications in TLE and AD.
The taxonomy has been based on the spatial level of analysis, namely:
o Voxel-based morphometry, where voxels are treated independently of each
other;
o Area-based morphometry, where an area is defined as an ensemble of voxels
not necessarily contiguous or belonging to the same structure;
o Structure-based morphometry, where a structure (commonly referred to as a
“region”) is defined as an ensemble of contiguous voxels belonging to a common
anatomical entity; and
o Volume-based morphometry, where a volume is a rigid 3D block, with or
without a priori knowledge as for its location. Limits of volume range from one
voxel to the whole brain.
While much research exists at the voxel and structure level, little has been done in the
other areas. This thesis will present in the following section novel methodologies to
address this shortcoming.
3.3.1 Voxel-based morphometry
Technique
Voxel-based morphometry (VBM, Figure 8) [30] consists in the statistical analysis of
generalized linear model results performed on a voxel-by-voxel basis on combined
cohorts of co-registered subject imaging data. VBM's detection accuracy is limited by
sources of spatially dependent and independent noise which compromise the statistical
results. Misregistration is a key factor which has been discussed [31, 32] and addressed
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using linear and nonlinear registration in recent VBM implementations [31]. The
techniques reviewed in the literature employ a single reference volume as their
registration target, usually an average of a large ensemble of images from healthy
individuals.
The de facto standard software package for VBM is that of Ashburner [30], which
consists in an adaptation of their statistical parametric mapping (SPM) technique,
initially designed for functional magnetic resonance imaging. The implementation that is
proposed in this thesis is based on fMRISTAT, developed by Keith Worsley for identical
purposes, and also in routine use at the MNI BIC.
In VBM, grey or white matter (GM,WM) concentration volumes across subjects are
analyzed in lieu of longitudinal activation maps. In order to perform meaningful cluster
analysis of the data, as opposed to peak finding, the issue of data nonisotropy must be
successfully resolved [33]; whereas many studies do not explicitly mention such
correction, fMRISTAT incorporates a correction methodology [33] before reporting
cluster statistics.
Cortical measurements, such as thickness [34-37], local deformations [38, 39] or a
combination of these measures [40], provide a voxel-based or local estimate of grey
matter integrity. While extremely useful to study progression in diseases like AD, they
are not necessarily well suited for detection of pathological effects in the temporal lobe.
This stands from a few simple facts: (a) restricting their analysis to the neocortex, they
do not include sub-cortical structures; (b) given the normal, anatomical variability in
sulcal folding, and the failure of all registration methods to date to take this variability
into account, it is hard to differentiate pathological signal from the anatomical noise; and
(c) given current image resolution, not all flattening or modeling techniques have been
able to give an accurate measure of the thickness of the hippocampus. Therefore while
it represents a key technique for longitudinal analyses, cortical thickness measurements
may not be adequate for early detection of AD (where the MTL is primarily affected) and
lateralization in TLE.
Application
It is now accepted that the hippocampus is not the only structure affected in TLE, as
grey matter (GM) and white matter (WM) atrophy in temporal and extra temporal
areas have been repeatedly demonstrated. It was shown in particular in VBM analyses
of T1-weighted (T1w) MRI from large groups of patients with TLE that reliably detected
and localized regions of GM and WM atrophy associated with the disease [41-45].
Voxel-based morphometry revealed that GM pathology in TLE extends beyond the
hippocampus, involving other limbic areas such as the cingulum and the thalamus, as
well as extralimbic areas, particularly the frontal lobe. White matter reduction was found
only ipsilateral to the seizure focus, including the temporopolar, entorhinal, and
perirhinal areas.
VBM research in mild to severe Alzheimer’s dementia [46-49] has demonstrated
atrophy of the hippocampus, temporal pole, and insula. For deep cerebral structures,
Karas [46] showed atrophy of the caudate head nuclei and medial thalami. In MCI,
recent studies [50, 51] showed that subjects with MCI had significant local reductions in
gray matter in the MTL, insula, and thalamus compared to NC subjects. By contrast,
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when compared to subjects with AD, MCI subjects had more GM in the parietal
association areas and the anterior and the posterior cingulate. The reader should note
however that, in the latter studies on MCI, no difference was made between converters
and non-converters to future AD.
Thompson [40, 52] has measured cortical thickness changes through the progression of
Alzheimer’s disease. Greatest deficits (20-30% loss) were mapped in the temporoparietal cortices. The sensorimotor and occipital cortices were comparatively spared (05% loss). Gray matter loss was greater in the left hemisphere. These results were
corroborated in other studies, such as Janke [39], who showed significant perturbations
in the deformation fields corresponding to the entorhinal cortex (EC) and hippocampus,
orbitofrontal and parietal cortex, and regions surrounding the sulci and ventricular
spaces, with earlier changes predominantly lateralized to the left hemisphere. This
distinct pattern of thinning is different from the one associated with normal aging [53,
54], which tends to involve frontal areas.
3.3.2 Area-based morphometry
Technique
T1w MR signal intensity can be used an indicator of the progression of a disease, where
subtle changes in the signal may indicate an underlying pathological process before
structure integrity is lost. Texture analysis allows the measurement of image properties
not readily accessible by visual analysis. Most texture methods are window-based, that
is local proprieties are extracted by computing different features within a fixed
dimension window moved onto the image.
Application
Webb [55] described an automatic method for identifying HA on MRI of TLE patients,
based on the analysis of image intensity differences between patients and controls
within an area of interest centered on the hippocampus. Normal variations of
hippocampal signal intensity were computed and used to distinguish patients with
significant HA. Since, Coan has also published work dealing with familial and nonfamilial TLE, using abnormal intensity patterns to differentiate with normal controls [56,
57].
Texture analysis has been used in epilepsy [58-60], and in Alzheimer’s research,
Freeborough [61] used a texture feature vector consisting of 260 measures derived from
the spatial gray-level dependence method. A stepwise discriminant analysis was
applied to the training set, to obtain a linear discriminant function, and obtained a
classification rate of 91%. No studies have been published on predicting conversion in
MCI patients.
3.3.3 Structure-based morphometry
Technique
Volumetry, that is volume measurement of a particular, bounded structure of interest, is
the primary indicator of structure integrity. Stereology, i.e. the technique of proper subsampling based on the Cavalieri principle [62], has been the preferred approach to
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calculating volumes before the advent of high-resolution images, where volumes can be
reliably estimated directly from the (often isotropic) voxel count and slice thickness.
Whether based on manual or automated segmentation (see Figure 10), a key difficulty in
this approach resides in the delineation of the structure of interest. Identification of
boundaries is made difficult by the inherent variability of the subject matter,
philosophical disagreements between investigators, differences in the visualization tools
and acquisition protocols. Pruessner [63] has investigated and pinpointed areas of
discrepancies that exist in the process for HC and AG, even within experts from the
same laboratory.
The automation of segmentation removes some of the subjective elements that
contribute to measurement noise. Pham [64] offers a review of this field that has seen
many advances in the last decade. Segmentation methodologies can be roughly divided
in two families: forward-based segmentation approaches aim to define boundaries
directly on the source image, whereas backward segmentation methods make use of
registration information to pull atlas information defined on a reference image back to
the source image, via the inverse transformation.
Deformation-based morphometry makes use of the properties of the dense nonlinear
deformation field mapping a source image to a common reference. This field is unique
to the image under study. The determinant of the Jacobian, calculated at the voxel level,
is a good approximation of the local volume change and therefore can be used to
measure clinically relevant parameters [38, 65]. Czernansky et al ([66], and later Wang
et al [67]) proposed a two-step process in which the hippocampus was manually
segmented from aMRI images, and then surface deformations modeled using Principal
Components Analysis. Medial sheets, as proposed by Styner or Joshi [68, 69], can be
used to determine intrinsic shape properties of the segmented structure of interest.
The major drawback of structure based approaches is their reliance on a priori spatial
knowledge about a particular structure or region of interest, thus necessitating some
form of segmentation, a process with its own limitations. Moreover, interrelations
between neighboring structures, critical in pathologies such as AD and TLE, are not
captured if only individual elements are measured.
Applications
Early work [70-73] in MR volumetry focused almost exclusively on the hippocampus and
the amygdala (AG). It became clear that the presence of hippocampal formation atrophy
in the temporal lobe as detected on MRI was correlated with the underlying pathology,
and affected the identification of potential candidates for epilepsy surgery. A period in
which closer scrutiny and evaluation of factors affecting segmentation protocols
followed [74]. Most of the current work analyses multiple structures beyond the HC/AG
complex, and therefore stresses the importance of the overall limbic network [6, 75]
In AD volumetry has been used to quantify and monitor dementia progression and rates
of regional atrophy [76]. It has been shown to document or quantify atrophy of the
hippocampus and entorhinal cortex, both of which occur early in the disease process
[9]. Visser et al [77] found that atrophy measures of many MTL structures help in
predicting decline from MCI to AD. Convit et al [78] reported that global MTL volume
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also helps in predicting decline. They also report that the apolipoprotein APOE ε4
genotype was not associated with decline, nor that other clinical baseline variables
examined predicted decline with sensitivities above 71%. A general trend found in the
literature is thus that the more MTL structure volumes are included in the analysis, the
better is the predictive ability for the decline from MCI to AD. Scahill et al [79] confirmed
these results using an automated volumetry technique. Likewise, Chan measured a
serie of temporal lobe structures (amygdala, hippocampus, entorhinal cortex,
parahippocampal gyrus, fusiform gyrus, and superior, middle, and inferior temporal
gyri), and reports symmetrical atrophy of the entorhinal cortex, hippocampus, and
amygdala, with no evidence of an anteroposterior gradient in the distribution of temporal
lobe or hippocampal atrophy [80]. However, Convit [78] and others also conclude that
the absence of atrophy does not exclude the development of dementia, which clearly
indicates that volumetry is not the answer for early detection as detectable atrophy
happens already late in the disease. In fact, Testa compared volumetry to VBM in AD,
and concluded that VBM is more sensitive to small changes; on the other hand their
sensitivity is higher together [81]. Csernansky used surface deformation models to
differentiate between small cohorts of normal aging and AD subjects. This analysis was
restricted to combining hippocampal volumes with hippocampal shape deformation
indicators. By using 5 eigenvectors as discriminants, they were able to differentiate
early AD patients on the basis of their expressed coordinates on those components.
Similar work was done for epilepsy in [82, 83]. Finally, medial sheets were used in the
evaluation of hippocampal shape in epilepsy [84, 85]
3.3.4 Volume-based morphometry
Technique
If we exclude volumetry of large structures (such as entire lobes), there seems to be no
technique that explicitly takes as an input a large, non-specific volume of interest, that
is, smaller than the brain itself.
The overarching paradigm seems to treat the images at some low-level (voxel,
individual structure) and then move up to the symbolic, abstract realm of volumes,
shape descriptors, and the like.
4. Hypothesis and goal
The working hypothesis underlying this thesis is that pathologies such as TLE and AD
affects the brain in such a way that, at the microscopic level, one can detect changes
using MR signal intensity, and, when those changes reach macroscopic proportions, in
changes in tissue morphology, captured via registration measures.
The goal of this thesis is to develop automated, objective classification methodologies
exploiting intensity and registration differences of MR images of patients when
compared to controls, and demonstrate the applicability of these techniques to two
different pathologies, namely TLE and AD.
It is aimed that these techniques be used in a clinical setting, potentially (as in screening
for AD) in hundreds of individual. This precluded methods involving expert interaction,
modalities other than MR (PET, SPECT), as well as complex sequences (MT, DTI,
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MRS). Finally, faced with the reality of reduced access to neuroimaging facilities in
western (and particularly Canadian) centers, diagnostic decision-making has to be
based on cross-sectional scans, rather than longitudinal acquisitions, even if relatively
close (e.g. < 12 months). Single time-point data immediately implies a comparative
method of classification where the accuracy of the measurement is less important than
the significance of the differences between groups.
5. Novel methodologies
5.1 Subjects – Epilepsy study
All patients underwent selective amygdala-hippocampectomy. Pre-operative T1w MR
3D images were acquired on a 1.5 T scanner using a T1-fast field echo sequence. All
global MRI data were processed to correct for intensity non-uniformity due to scanner
variations [86], linearly registered into stereotaxic space and resampled onto a 1mm
isotropic grid [87]. Hippocampal atrophy (HA) ipsilateral to the seizure focus, measured
via Manual MRI volumetry by a neuroanatomical expert (N. Bernasconi) was the basis
for selection of 78 of those patients; the remaining had normal HC volumes (NV). On
this basis, patients were separated into four groups: left or right seizure focus HA or NV.
Normal control subjects were recruited and scanned at the same time as the TLE
patients, and consist of a group of 47 individuals. The following table shows the
breakdown of patients per group:
Left focus
Right focus
Total
HA
41
37
78
NV
27
20
47
Total
68
57
125
For a subset of 39 TLE HA patients we were able to obtain post-operative follow-up
information of at least 12 months, on which basis they were consolidated in two groups:
25 seizure free, positive outcome patients and 14 negative outcome patients.
5.2 Subjects – Alzheimer’s dementia study
It is proposed to study three different AD populations for this thesis:
A. German cohort: the first group consists in 44 consenting individuals evaluated at
the Alzheimer Memorial Center, Dementia Research & Neuroimaging Section,
Ludwig-Maximilian University Munich, Germany (PI: J.C. Pruessner, S. Teipel, H.
Hampel): 15 clinical AD [age 70(8)], 7 MCI [age 74(8)] and 22 NA [age 62(9)].
Patient and normal aging scans were interleaved due to the consecutive nature
of the individual selection; this resulted in a significant age difference between
NA and AD or MCI, while there were no differences between AD and MCI. T1w
MRI were acquired after informed consent on a 1.5T Siemens Magnetom Vision
scanner (3D sequence, TR=11.6ms, TE=4.9ms, FA = sagittal, 256 (SI) x 204
(AP) mm pixels, 1mm slices). All global MRI data were processed to correct for
intensity non-uniformity due to scanner variations [86], linearly registered into
stereotaxic space and resampled onto a 1mm isotropic grid [87].
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B. Canadian cohort: the second group consists in ~180 individuals that were part
of an enrolment study (PI: H. Chertkow, M.D.) at the memory clinic of the Lady
Davis Institute (Jewish General Hospital, Montreal University Health Center) with
baseline scans acquired between 1993 and 1998. There is almost an equal
proportion of AD, MCI and NA subjects at baseline. At time of last follow-up
nearly 77% of MCI individuals had converted to AD.
C. Italian cohort: the final group consists in 58 NA, 30 to 40 AD patients and 58
subjects with MCI, age and sex-matched. All individuals are enrolled in an MCI
study from the Centro Alzheimer, Brescia, Italy (PI: G. Frisoni, M.D.). The data
includes extensive neurological, neuropsychological and other clinical data with
up to 24 months follow-up, as well as T1w baseline scans for all subjects. All
subjects underwent neuroradiological, clinical and neuropsychological evaluation.
Cognitive function has been assessed using multiple criteria scales, including
MMSE and depression symptoms assessment. Follow-up ranges from 0 to 24
months. MR images consist in T1-weighted volumes acquired on a 1.0 Tesla
Philips Gyroscan (PG) in Brescia and 1.0 Tesla Siemens Impact (SI) in Verona.
MR images were acquired with gradient echo 3D technique as follow: TR=19.7
ms, TE=6.9ms, field of view=240mm, slice thickness 1.3mm, pixel size 0.93mm
in Brescia; TR=11.4ms, TE=4.4ms, field of view 250mm, slice thickness 1.3mm
with pixel size 0.98mm in Verona.
5.3 Novel methodology for the prediction of therapeutic effect
Relating our global hypothesis to prediction, and assuming a constant output from the
therapy of choice, we believe there exists brain areas linked to treatment outcome with
information seen on T1w MRI that can be used for prediction purposes.
The technique is an extension of VBM (see Figure 9). The objective is to determine
areas of GM, WM concentration that would be related to treatment by performing a
voxel-based morphometry study comparing individuals from two groups of patients with
different outcome. Using the combination of significant clusters as an area of interest, it
is proposed to use the mean GM and WM concentration of all voxels within that area as
classification features. The accuracy of the linear discriminant classifier is assessed by
performing a leave-one-out analysis to categorize subjects.
5.4 Novel methodology for improved atlas definition and structure
segmentation
Validation of automated structure segmentation is necessary in order to determine if
such a technique can be compared to manual or expert delineation. The goal is to
measure the inter-rater variability of automated segmentation when compared to
manually segmented structure, and see if this variability significantly differs from that
exhibited by multiple experiences raters. As a point of reference, Pruessner et al
conducted a segmentation study where 5 different raters manually segmented the
hippocampus and amygdala in a cohort of 40 neurologically healthy young volunteers,
taken from the ICBM database. The combined (left and right, HC and AG), inter-rater
variability, assessed using the kappa overlap coefficient was 0.86, indicative of good
agreement.
14
It is proposed to validate the ANIMAL segmentation methodology, where an atlas
defined on a reference image is propagated back to the individual image via the inverse
nonlinear dense deformation field optimally mapping common features from both
images. As mentioned in a previous publication [88] the proper definition of the atlas on
the reference target strongly influences the resulting segmentation. In order to build
such an atlas, it is proposed to use an iterative approach (see Figure 11):
1) T1w subject images from a training group of 40 individuals will be nonlinearly
registered to the ICBM “6th generation” target (see [89] for more details on the
construction of such a target) using optimized ANIMAL parameters [90];
2) HC and AG segmentation will be performed by a neuroanatomist on this ICBM
“6th generation” target (JCP);
3) The HC and AG atlas will be back-propagated to the individual images via the
inverse deformation field;
4) Areas of difference between the HC and AG atlas in subject space will be
assessed against previously manually segmented volumes (same definition,
same rater (JCP));
5) Areas of differences will be transformed into the reference space. An iterative
process may be used (e.g. expectation-maximization algorithm) to include other
voxels in the atlas, with the goal of increasing a measure of goodness of fit (e.g.
volume, kappa overlap coefficient, other).
5.5 Novel methodology for the classification of neurological diseases
The hypothesis behind the proposed method is that a given non-specific Volume of
Interest (VOI) contains sufficient discriminatory information in the form of image intensity
and registration features to accurately classify groups of subjects.
The method can be summarized as follows (see Figure 12). First, a normative
multidimensional eigenspace is created by uniting results from two distinct Principal
Component analyses of the following data: (a) linearly registered intensity images of the
VOI; and (b) an approximation of the determinant of the Jacobian matrix of the
deformation field for the given VOI. The deformation fields are obtained by non-linear
registration of the VOI with a common reference image.
For the initial applications, it is proposed to create a normal, non-pathological
eigenspace by using processed data from a large training group of normal control
subjects. Secondly, VOIs from study subjects, including patients and additional normal
controls, are projected in the multidimensional eigenspace created. Logistic regression
is used to identify eigenvectors with the most discriminative power. The last step
consists in linear discriminant analyses (LDA) used to classify the study subjects, based
on their expressed eigencoordinates. The aim is to derive diagnostic information
correlated to a particular distribution of coordinates along a Principal Component (PC).
5.6 Novel methodology for the prediction of clinical evaluation scores
from MR scans
This methodology is an expansion of the VOI characterization technique discussed
above. Results of classification experiments (see below) have proven that
15
eigencoordinates from selected eigenvectors could be used as discriminant features in
a classification scheme. It is now wished to move from the accurate grouping of data to
one predicting the state of a clinical variable.
It is proposed to use normalized eigencoordinates on the selected eigenvectors to
define a multidimensional distance and assess the correlation of this distance with
important clinical parameters (e.g. MMSE in AD research). If the correlation is strong,
then the predictive ability of this distance could be measured; alternatively, multiple
regression could be used on the eigencoordinates. It would therefore provide a link
between often subtle morphological changes as measured by intensity and shape
variations, and cognitive function variations.
6. Experimental plan and results
6.1 Experiment 1 – VBM of TLE HA subjects
The purpose of this experiment was to determine whole-brain GM and WM changes in
TLE and to investigate the relationship between these abnormalities and clinical
parameters using a standard VBM approach. While GM concentration abnormalities
had been reported in the literature, WM differences had not.
We studied 85 patients with pharmacologically intractable TLE and unilateral
hippocampal atrophy and 47 age- and sex-matched healthy control subjects. The
seizure focus was right sided in 40 patients and left sided in 45. Student's t test
statistical maps of differences between patients' and controls' GM and WM
concentrations were obtained using a general linear model. A further regression against
duration of epilepsy, age of onset, presence of febrile convulsions, and secondary
generalized seizures was performed with the TLE population.
Voxel-based morphometry revealed that GM pathology in TLE extends beyond the
hippocampus involving other limbic areas such as the cingulum and the thalamus, as
well as extralimbic areas, particularly the frontal lobe. White matter reduction was found
only ipsilateral to the seizure focus, including the temporopolar, entorhinal, and
perirhinal areas. This pattern of structural changes is suggestive of disconnection
involving preferentially frontolimbic pathways in patients with pharmacologically
intractable TLE.
Results from this experiment were published in:
o N. Bernasconi, S. Duchesne A. Janke, J. Lerch, D.L. Collins, A. Bernasconi
“Whole-brain voxel-based statistical analysis of gray matter and white matter in
temporal lobe epilepsy”, NeuroImage, 23(2):717-723, 2004
6.2 Experiment 2 – VBM of TLE NV subjects
The purpose of this experiment was to determine whole-brain GM and WM changes in
TLE patients exhibiting normal hippocampal volumes and to investigate the relationship
between these abnormalities and clinical parameters. We studied 47 patients with
pharmacologically intractable TLE and no hippocampal atrophy (27 left sided focus) to
16
47 age- and sex-matched healthy control subjects. In the second phase of this
experiment we compared the NV patients to 85 HA patients (41 left sided focus).
VBM results for these experiment, along with the clinical relevance of the GM and WM
abnormalities will be assessed and discussed in a manuscript under preparation by
collaborators.
6.3 Experiment 3 – TLE surgical outcome prediction
We propose to study pre-operative T1-weighted MRI of TLE patients with HA who were
followed by the same physician (Dr A. Olivier, MNH/MNI) and had undergone similar
surgical treatment (selective amygdala-hippocampectomy). A population of 39
intractable TLE patients was studied with post-operative follow-up of at least 12 months.
On that basis they were consolidated in two outcome groups: seizure free (positive
outcome, n=25) or not seizure free (negative outcome, n=14).
The goal of this experiment was to use area-based morphometry to attempt the
prediction of surgical outcome based on pre-operative T1w MR scans. To this end a
standard VBM analysis of GM and WM concentration was performed to identify
differences between the two groups. Voxels above the significance threshold for
clusters were consolidated into a single area of interest. For each patient, mean GM
and WM concentration was calculated in those respective areas. Linear discriminant
analysis was used as a classifier using mean GM and WM concentration features in a
leave-one-out approach to predict surgical outcome.
Areas of GM and WM concentration differences that were related to surgical outcome
were successfully located using a standard VBM approach. GM concentration changes
were primarily located in the left lateral temporal neocortical region, while more
extensive changes were found in left lateral temporal and occipital WM. The fact that
there exist statistically significant areas of differences between the two groups is an
indication that such information could be used for predictive purposes.
The classification accuracy of the classifier was excellent. The average GM and WM
concentrations of negative outcome patients in the area of interest are lower than that of
positive outcome patients. A closer examination of the surgical variables (approach,
length) must be made before coming to final conclusions based on these results.
Regardless, the method is successful at detecting changes between groups and
predicting therapeutic outcome based on those changes.
These results were published as a conference proceedings in:
o S. Duchesne et al, “TLE surgery outcome prediction “, Proc. Medical Image
Computing and Computer-Assisted Intervention, C. Barillot, D.R. Haynor, P.
Hellier, Eds., Springer-Verlag, LNCS 3217:696-702, 2004
A final manuscript is under preparation, to be submitted to Nature Medicine in early
2005.
17
6.4 Experiment 4 – Validation of automated structure segmentation in NC
The methodology defined above will be used to define a final atlas and segment the HC
and AG (left, right) on a control group of 40 neurologically healthy, young individuals
taken from the ICBM data set. Kappa overlap statistics (see [88]) and pair-mached t-test
will be used to assess the inter-rater variability between manually and automatically
segmented structures.
This method remains to be developed and has not been published.
6.5 Experiment 5 – Lateralization of seizure focus in TLE
We have performed classification experiments to assess the classifier's performance in
detecting and lateralizing the seizure focus in temporal lobe epilepsy. Our model was
built using a training set of 152 normal control subjects from the ICBM database. We
used as a control group for our classification 47 matched normal controls, and the
patient population of 125 subjects was separated in four groups: 41 with left TLE and
HA, 37 with right TLE and HA, for a total of 78 TLE HA patients; and 27 left TLE with
NV, 20 right TLE with NV, for a total of 47 TLE patients with NV. The classification
experiments that we proposed were aimed at testing the system's ability for detecting
differences between groups.
Our results indicate that the position information (eigencoordinates) of new data once
projected in multidimensional feature domains is sufficient to adequately discriminate
between our two populations. We can classify and lateralize seizure focus in HA and NV
populations with almost perfect accuracy (> 95%).
Preliminary results based on intensity modeling of the VOI and for the purpose of
differentiating TLE HA against normal controls were published as a conference
proceeding:
o S. Duchesne, N. Bernasconi, A. Bernasconi, D.L. Collins “On the classification of
Temporal Lobe Epilepsy based on MR image appearance”, Proc. International
Conference on Pattern Recognition, IEEE Computer Society, 1: 520-523,2002
An interim version of the method (intensity and trace features), with results on TLE
seizure focus lateralization in HA patients, was published as a conference proceeding:
o S. Duchesne, N. Bernasconi, A. Bernasconi, D.L. Collins “TLE lateralization
using MR image intensity and registration features”, Proc. Medical Image
Computing and Computer-Assisted Intervention, Springer-Verlag, 2879(1):367374 2003
A manuscript detailing the finalized method as discussed above (including changes to
the statistical analysis) along with final results on TLE seizure focus lateralization in HA
and NV patients, is under preparation for submission to NeuroImage (Dec 2004).
Based on these promising results in TLE, a grant request was submitted to and
accepted in the CIHR POP program (PI: D.L. Collins). We received 100K$ funding to
apply this technique on early AD detection.
18
Finally, an application for patent has been submitted to the U.S. Patent Office on 18
Nov 2004:
o D.L. Collins, S. Duchesne, Inventors, “Systems and Methods of Classification
Utilizing Intensity and Spatial Data ”, U.S. Provisional Patent
6.6 Experiment 6 – Classification of subjects into normal aging, MCI and AD
This experiment was performed using the German AD cohort, consisting of 15 clinical
AD, 7 MCI and 22 NA with T1w MRI acquired after informed consent on a 1.5T Siemens
Magnetom Vision scanner (3D sequence, TR=11.6ms, TE=4.9ms, FA = sagittal, 256
(SI) x 204 (AP) mm pixels, 1mm slices).
All scans were corrected for intensity inhomogeneity, linearly registered in stereotaxic
space and intensity normalized. Rectangular volumes of interest (VOI) were defined on
the left and right MTL (80x52x60 voxels). Each VOI was further linearly and nonlinearly
registered to a reference target image. Two features were defined for classification: the
linear registered, normalized intensity and the trace of the Jacobian of the nonlinear
deformation fields, providing a measure of local volume change.
Normative spaces were created from 152 normal young subjects using principal
components analysis of the intensity and trace VOIs. Pre-processed scans from the
cohort subjects were projected in the normative spaces. Logistic regression (χ2 < 4.04 ,
P < 0.045) was used to identify significant eigenvectors that were then retained for
forward stepwise linear discriminant analyses. With 12 eigenvectors (P-to-enter < 0.2)
the classifier sensitivity to AD was 93% and to MCI 100%, while it achieved 100%
specificity.
These results indicate that MR data projected in multidimensional feature domains can
adequately discriminate NA, AD and MCI populations. This single-scan, practical and
objective method holds promise for AD or MCI detection from normal aging.
These initial results on NA, AD and MCI classification are being submitted as
conference proceedings:
o S. Duchesne, J.C. Pruessner, S. Teipel, H. Hampel, D.L. Collins, “Successful
AD and MCI differentiation from normal aging via automated analysis of MR
image features”, submitted to Alzheimer’s Association International Conference
on Prevention of Dementia, Washington D.C., June 2005
A manuscript based on those results is under preparation, to be submitted to Am. J.
Psychiatry (Jan 2005).
Further work on the Canadian cohort remains to be completed in order to accurately
predict MCI conversion to AD using clinical quality scans.
6.7 Experiment 7 – Prediction of cognitive decline in MCI
It is proposed to use the eigendistance technique, described in section 5.6, to assess
and predict cognitive decline in MCI patients. To this end the Italian cohort (G. Frisoni,
M.D.) will be analysed. At the time of writing, the clinical follow-up reported that 17 out
19
of 38 MCI patients in that cohort had worsened, that is they had lost 2 (or 4) MMSE
score points at 12 (or 24) months follow-up. The goal of this experiment will be to
assess and if possible predict this decline in those MCI patients.
This work remains to be completed.
7. Contributions
This thesis proposes novel methodologies for the study of various neurological diseases
and disorders as well as new applications of existing techniques in those same areas.
7.1 Novel methodologies
Novel methodologies include:
o Area-based morphometry
o Iterative atlas construction
o VOI-based morphometry
o Prediction of clinical evaluation scores from MR data
7.2 Novel applications
Applications of novel and existing methodologies include:
o VBM analysis of GM and WM in TLE HA;
o VBM analysis of GM and WM in TLE NV;
o VBM analysis of GM and WM in converting and non-converting MCI;
o TLE surgical outcome prediction using area-based moprhometry;
o Validation of automated hippocampal and amygdala segmentation in
normal controls;
o Lateralization of seizure focus in TLE using volume-based morphometry;
o Differentiation of AD, MCI from normal aging using volume-based
morphometry;
o Prediction of conversion from MCI to AD using volume-based
morphometry;
o Prediction of cognitive decline in MCI
7.3 Publications to date
o N. Bernasconi, S. Duchesne A. Janke, J. Lerch, D.L. Collins, A. Bernasconi
“Whole-brain voxel-based statistical analysis of gray matter and white matter in
temporal lobe epilepsy”, NeuroImage, 23(2):717-723, 2004
o S. Duchesne et al, “TLE surgery outcome prediction “, Proc. Medical Image
Computing and Computer-Assisted Intervention, C. Barillot, D.R. Haynor, P.
Hellier, Eds., Springer-Verlag, LNCS 3217:696-702, 2004
o S. Duchesne, N. Bernasconi, A. Bernasconi, D.L. Collins “On the classification of
Temporal Lobe Epilepsy based on MR image appearance”, Proc. International
Conference on Pattern Recognition, IEEE Computer Society, 1: 520-523,2002
20
o S. Duchesne, N. Bernasconi, A. Bernasconi, D.L. Collins “TLE lateralization
using MR image intensity and registration features”, Proc. Medical Image
Computing and Computer-Assisted Intervention, Springer-Verlag, 2879(1):367374 2003
o D.L. Collins, S. Duchesne, Inventors, “Systems and Methods of Classification
Utilizing Intensity and Spatial Data ”, U.S. Provisional Patent
o S. Duchesne, J.C. Pruessner, S. Teipel, H. Hampel, D.L. Collins, “Successful
AD and MCI differentiation from normal aging via automated analysis of MR
image features”, submitted to Alzheimer’s Association International Conference
on Prevention of Dementia, Washington D.C., June 2005
8. Conclusion
Imaging can assist in accurate and early diagnosis of many neurological diseases as
well as identify possible targets for drug therapy. The proposed methods will facilitate
the understanding of TLE, therapy follow-up and pre-surgical evalution and planning.
They will also improve our ability to detect AD early and to differentiate it from normal
aging or MCI; optimal therapeutic care can be provided if an early diagnosis is given,
and can help in delivering drug therapy which may slow down the progression of the
disease. For these reasons a logical benefactor of the proposed work would be the
Canadian health network. In fact, this entire research project may have a major
international impact with wide potential use in epilepsy and memory clinics.
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10. Figures
26
Epilepsy - Incidence
EPILEPSY
CANADIAN POPULATION
3%
INTRACTABLE
20%
TEMPORAL LOBE
75%
HIPPOCAMPAL ATROPHY (HA)
80 %
McLachlan 2001
Figure 1 - Epilepsy incidence in the general population with break-down into intractable,
temporal lobe epilepsy patients with hippocampal atrophy. There remains 20% of
intractable TLE patients for whom the seizure focus cannot be lateralized using standard
MR volumetry techniques.
Epilepsy - Etiology
L
R
NC
L
R
TLE
MRI courtesy A. Bernasconi
Figure 2 - Etiology of temporal lobe epilepsy is characterized by neuronal loss and gliosis in
the hippocampal and para-hippocampal region, evident when comparing a T1W MR
transverse image from a normal control subject to a similar image from a TLE patient.
27
Epilepsy - Lateralization methods
Surgically implanted EEG
Automated aMRI analysis
Histopathology
Accuracy
Single Photon Emission CT
Positron Emission Tomography
aMRI structure volumetry
Surface EEG
Clinical signs
Invasiveness
Figure 3 - Positioning of automated anatomical MR image analysis as a non-invasive and
accurate method for lateralization of seizure focus in temporal lobe epilepsy patients (HA
or NV).
Alzheimer - Incidence/Prevalence
All dementias
Alzheimer’s - 75%
.5%
1%
2%
4%
Others - 25%
65 - 69 yrs
70 - 74 yrs
75 - 79 yrs
80 - 84 yrs
7%
Wimo 2004
Cdn Wkg Gp 2000
85 + yrs
Figure 4 - Incidence and world prevalence of Alzheimer's dementia shows an increasing
and alarming trend as the life expectancy increases and populations age.
28
Alzheimer’s Dementia - Etiology
Tangles
Hippocampus (HC)
Plaques
http://www.neuropat.dote.hu
http://www.biocell-interface.com
Figure 5 - Etiology of AD shows neurofibrillary tangles and plaques (bottom right) from
abnormal protein deposition leading to neuronal loss. Pathological studies have
demonstrated that this initially affects the hippocampus (top right), the medial temporal
lobes before resulting in widespread tissue loss (top left, 72 y.o. male AD patient, T1W MR
coronal slice).
MMSE
Alzheimer - Progression
30
20
Time ?
0y
10 y
Normal
aging
Normal
aging
MCI
MMSE 24-30
MCI
Mild AD
MMSE 20-23
Moderate AD
MMSE 10-19
10
Severe AD
MMSE 0-9
D
Adapted from Petrella 2003
Figure 6 - While normal aging individuals will experience a small decline in cognitive
functions over time, MCI patients compose an at-risk group of converting to AD. From
clinical diagnosis of dementia, the prognosis is 8-10 years.
29
Treatment efficacy
Alzheimer - Early detection
Histopathology
Automated aMRI analysis
Accuracy
Positron Emission Tomography
aMRI structure volumetry
Single Photon Emission CT
Apolipoprotein / CSF biopsy
Treatment
success ∝
early
detection
Neuropsychological testing
Clinical signs (80%)
T-5?
T
D
Figure 7 - Positioning of automated anatomical MRI analysis as an early and accurate
means of predicting cognitive decline and conversion to AD in at-risk populations.
Voxel-based morphometry
Volume from scanner to
Talairach space
Concentration
threshold
Classification
Intensity inhomogeneity
correction and normalization
Concentration
map smoothing
Global LIN and
NONLIN registration
Nonisotropy
Correction
Generalized
Linear modeling
Thresholded
T-stat image
N
ICBM 152 avg in
stereotaxic space
Figure 8 - Standard approach for voxel-based morphometry (Ashburner, 2000) consisting
in pre-processing of images followed by classification and GM/WM smoothing. The
concentration images are then statistically analysed via general linear modeling. Significant
clusters from the resulting t-stat images are then computed using random Gaussian field
theory.
30
Area-based morphometry
Volume from scanner to
Talairach space
Intensity inhomogeneity
correction and normalization
Thresholded
T-stat image
Generalized
Linear modeling
Area selection
Classification
N
Global LIN and
NONLIN registration
Concentration
map smoothing
Area Measure
Classification
+…
N
ICBM 152 avg in
stereotaxic space
Duchesne, MICCAI, 2004
Figure 9 - Schematic diagram for novel area-based morphometry methodology. The first
step consists in a VBM study between two groups with different therapy outcome to
identify the area of interest. GM and WM measures are then computed from voxels within
those areas, and presented to a linear discriminant classifier.
Structure-based morphometry
Volume from scanner to
Talairach space
ANIMAL
NONLIN
registration
ICBM 152 avg in
stereotaxic space
Deformation
Field
Atlas definition
Inhomogeneity correction
Global LIN registration
AB
NONLIN
registration
ICBM 152 avg in
stereotaxic space
Principal
Components
Analysis
Appearance
Matching
Structure
segmentation
ICBM 152 avg in
stereotaxic space
Collins, IJPRAI 1997
Duchesne, NeuroImage 2002
Figure 10 - Schematic diagram of two possible atlas-based segmentation protocols
(ANIMAL, AB) involving the computation of a nonlinear deformation field mapping
features in source to reference image, and allowing the back-propagation of structures
defined on the reference to the source space.
31
Iterative atlas construction
Volume from scanner to
Talairach space
NONLIN
registration
Def field
Atlas definition
Inhomogeneity correction
ICBM 152 6th
generation avg
Iterate until convergence
Global LIN registration
Differences with
manual seg
Move diff to ICBM
6th gen space
ICBM 152 avg in
stereotaxic space
Average diff; select
new voxels based
on threshold
Cropping and LIN registration
Structure
segmentation
ICBM 152 6th
generation avg
Figure 11- Schematic diagram of proposed novel methodology for iterative atlas
construction. After an initial estimate is provided, iterative optimization of a comparison
measure is made by adding or substracting voxels to the atlas.
Volume-based morphometry
Intensity
Volume from scanner to
Talairach space
LIN registration
Normalization
Principal
Components
Analysis
New Data
Projection
Inhomogeneity correction
Logistic Regression /
Linear Discriminant
Analyses
ICBM 152 avg in
stereotaxic space
Global LIN registration and
cropping to VOI
Trace
NONLIN
registration
ICBM 152 avg in
stereotaxic space
PC #2
Trace
calculation
X: LHA
O: RHA
Principal
Components
Analysis
ICBM 152 avg in
stereotaxic space
Duchesne, MICCAI 2003
Figure 12 - Schematic diagram of novel volume-based morphometry methodology. A
normative eigenspace is first created via principal components analysis of processed data
(normalized intensity, trace) for a rectangular volume of interest (VOI) from a large
32
training group of normal control subjects. Secondly, VOIs from study subjects, including
patients and additional normal controls, are projected in the multidimensional eigenspace
created. Logistic regression is used to identify eigenvectors with the most discriminative
power. The last step consists in linear discriminant analyses (LDA) used to classify the
study subjects, based on their expressed eigencoordinates.
33