With the advances of PET tracers for β-Amyloid (Aβ) detection in neurodegenerative diseases, automated quantification methods are desirable. For clinical use, there is a great need for PET-only quantification method, as MR images are not always available. In this paper, we validate a previously developed PET-only quantification method against MR-based quantification using 6 tracers: 18F-Florbetaben (N=148), 18F-Florbetapir (N=171), 18F-NAV4694 (N=47), 18F-Flutemetamol (N=180), 11C-PiB (N=381) and 18F-FDG (N=34). The results show an overall mean absolute percentage error of less than 5% for each tracer. The method has been implemented as a remote service called CapAIBL (http://milxcloud.csiro.au/capaibl). PET images are uploaded to a cloud platform where they are spatially normalised to a standard template and quantified. A report containing global as well as local quantification, along with surface projection of the β-Amyloid deposition is automatically generated at the end of the pipeline and emailed to the user.
Kaikai Shen, Vincent Doré, Stephen Rose, Jurgen Fripp, Katie McMahon, Greig de Zubicaray, Nicholas Martin, Paul Thompson, Margaret Wright, Olivier Salvado
The aim of this paper is to assess the heritability of cerebral cortex, based on measurements of grey matter (GM) thickness derived from structural MR images (sMRI). With data acquired from a large twin cohort (328 subjects), an automated method was used to estimate the cortical thickness, and EM-ICP surface registration algorithm was used to establish the correspondence of cortex across the population. An ACE model was then employed to compute the heritability of cortical thickness. Heritable cortical thickness measures various cortical regions, especially in frontal and parietal lobes, such as bilateral postcentral gyri, superior occipital gyri, superior parietal gyri, precuneus, the orbital part of the right frontal gyrus, right medial superior frontal gyrus, right middle occipital gyrus, right paracentral lobule, left precentral gyrus, and left dorsolateral superior frontal gyrus.
KEYWORDS: Alzheimer's disease, Expectation maximization algorithms, Medical research, Image segmentation, Magnetic resonance imaging, Brain, 3D image processing, Visualization, Positron emission tomography, In vivo imaging
β-amyloid has been shown to play a crucial role in Alzheimer's disease (AD). In vivo β-amyloid imaging using
[11C]Pittsburgh compound Β (PiB) positron emission tomography has made it possible to analyze the relationship
between β-amyloid deposition and different pathological markers involved in AD. PiB allows us to stratify the
population between subjects which are likely to have prodromal AD, and those who don't. The comparison of the
cortical thickness in these different groups is important to better understanding and detect the first symptoms of the
disease which may lead to an earlier therapeutic care to reduce neurone loss.
Several techniques have been developed to compare the cortical volume and/or thickness between AD and HC groups.
However due to the noise introduced by the cortical thickness estimation and by the registration, these methods do not
allow to unveil any major different when comparing prodromal AD groups with healthy control subjects group. To
improve our understanding of where initial Alzheimer neurodegeneration occurs in the cortex we have developed a
surface based technique, and have applied it to the discrimination between PIB-positive and PiB-negative HCs. We first
identify the regions where AD patients show high cortical atrophy by using an AD/PiB- HC vertex-wise T-test. In each
of these discriminating regions, comparison between PiB+ HC, PiB- HC and AD are performed. We found some
significant differences between the two HC groups in the hippocampus and in the temporal lobe for both hemisphere and
in the precuneus and occipital regions only for the left hemisphere.
In order to fit an unseen surface using statistical shape model (SSM), a correspondence between the unseen
surface and the model needs to be established, before the shape parameters can be estimated based on this
correspondence. The correspondence and parameter estimation problem can be modeled probabilistically by
a Gaussian mixture model (GMM), and solved by expectation-maximization iterative closest points (EM-ICP)
algorithm. In this paper, we propose to exploit the linearity of the principal component analysis (PCA) based
SSM, and estimate the parameters for the unseen shape surface under the EM-ICP framework. The symmetric
data terms are devised to enforce the mutual consistency between the model reconstruction and the shape surface.
The a priori shape information encoded in the SSM is also included as regularization. The estimation method
is applied to the shape modeling of the hippocampus using a hippocampal SSM.
The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). Using brain
Magnetic Resonance (MR) images, we can investigate the effect of AD on the morphology of the hippocampus.
Statistical shape models (SSM) are usually used to describe and model the hippocampal shape variations among
the population. We use the shape variation from SSM as features to classify AD from normal control cases
(NC). Conventional SSM uses principal component analysis (PCA) to compute the modes of variations among
the population. Although these modes are representative of variations within the training data, they are not
necessarily discriminant on labelled data. In this study, a Hotelling's T 2 test is used to qualify the landmarks
which can be used for PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination
ability of these predictors is evaluated in terms of their classification performances using support vector
machines (SVM). Using only landmarks statistically discriminant between AD and NC in SSM showed a better
separation between AD and NC. These predictors also showed better correlation to the cognitive scores such as
mini-mental state examination (MMSE) and Alzheimer's disease assessment scale (ADAS).
Multiatlas based segmentation-propagation approaches have been shown to obtain accurate parcelation of brain
structures. However, this approach requires a large number of manually delineated atlases, which are often not
available. We propose a supervised method to build a population specific atlas database, using the publicly
available Internet Brain Segmentation Repository (IBSR). The set of atlases grows iteratively as new atlases
are added, so that its segmentation capability may be enhanced in the multiatlas based approach. Using a
dataset of 210 MR images of elderly subjects (170 elderly control, 40 Alzheimer's disease) from the Australian
Imaging, Biomarkers and Lifestyle (AIBL) study, 40 MR images were segmented to build a population specific
atlas database for the purpose of multiatlas segmentation-propagation. The population specific atlases were used
to segment the elderly population of 210 MR images, and were evaluated in terms of the agreement among the
propagated labels. The agreement was measured by using the entropy H of the probability image produced
when fused by voting rule and the partial moment μ2 of the histogram. Compared with using IBSR atlases, the
population specific atlases obtained a higher agreement when dealing with images of elderly subjects.
Small animal registration is an important step for molecular image analysis. Skeleton registration from whole-body or
only partial micro Computerized Tomography (CT) image is often performed to match individual rats to atlases and
templates, for example to identify organs in positron emission tomography (PET). In this paper, we extend the shape
context matching technique for 3D surface registration and apply it for rat hind limb skeleton registration from CT
images. Using the proposed method, after standard affine iterative closest point (ICP) registration, correspondences
between the 3D points from sour and target objects were robustly found and used to deform the limb skeleton surface
with thin-plate-spline (TPS). Experiments are described using phantoms and actual rat hind limb skeletons. On animals,
mean square errors were decreased by the proposed registration compared to that of its initial alignment. Visually,
skeletons were successfully registered even in cases of very different animal poses.
This paper presents a novel method to reduce the effects of interleaving motion artefacts in single-plane MR scanning of
the pelvic region without the need for k-space information. Interleaved image (or multipacket) acquisition is frequently
used to reduce cross-talk and scanning time during full pelvic MR scans. Patient motion during interleaved acquisition
can result in non-linear "staircase" imaging artefacts which are most visible on sagittal and coronal reconstructions.
These artefacts can affect the segmentation of organs, registration, and visualization. A fast method has been
implemented to replace artefact affected slices in a packet with interpolated slices based on Penney et al (2004) whose
method involves the registration of neighbouring slices to obtain correspondences, followed by linear interpolation of
voxel intensities along the displacement fields. This interpolation method has been applied to correct motion affected
MRI volumes by firstly creating a new volume where every axial slice from the artefact affected packet is removed and
replaced with an interpolated slice and then secondly for each of these slices, 2D non-rigid registration is used to register
each original axial slice back to its matching interpolated slice. Results show visible improvements in artefacts
particularly in sagittal and coronal image reconstructions, and should result in improved intensity based non-rigid
registration results between MR scans (for example for atlas based automatic segmentation). Further validation was
performed on simulated interleaving artefacts which were applied to an artefact free volume. Results obtained on
prostate cancer radiotherapy treatment planning contouring were inconclusive and require further investigation.
Despite the increasing use of 11C-PiB in research into Alzheimer's disease (AD), there are few standardized analysis
procedures that have been reported or published. This is especially true with regards to partial volume effects (PVE) and
partial volume correction. Due to the nature of PET physics and acquisition, PET images exhibit relatively low spatial
resolution compared to other modalities, resulting in bias of quantitative results. Although previous studies have applied PVE correction techniques on 11C-PiB data, the results have not been quantitatively evaluated and compared against uncorrected data. The aim of this study is threefold. Firstly, a realistic
synthetic phantom was created to quantify PVE. Secondly, MRI partial volume estimate segmentations were used to improve voxel-based PVE correction instead of using hard segmentations. Thirdly, quantification of PVE correction was evaluated on 34 subjects (AD=10, Normal Controls (NC)=24), including 12 PiB positive NC. Regional analysis was performed using the Anatomical Automatic Labeling (AAL) template, which was registered to each patient. Regions of interest were restricted to the gray matter (GM) defined by the MR segmentation. Average normalized intensity of the neocortex and selected regions were used to evaluate the discrimination power between AD and NC both with and without PVE correction. Receiver Operating Characteristic (ROC) curves were computed for the binary discrimination task. The phantom study revealed signal losses due to PVE between 10 to 40 % which were mostly recovered to within 5% after correction. Better classification was achieved after PVE correction, resulting in higher areas under ROC curves.
The segmentation of the bones from MR images is useful for performing subsequent segmentation and quantitative
measurements of cartilage tissue. In this paper, we present a shape based segmentation scheme for the bones
that uses texture features derived from the phase and intensity information in the complex MR image. The
phase can provide additional information about the tissue interfaces, but due to the phase unwrapping problem,
this information is usually discarded. By using a Gabor filter bank on the complex MR image, texture features
(including phase) can be extracted without requiring phase unwrapping. These texture features are then analyzed
using a support vector machine classifier to obtain probability tissue matches. The segmentation of the bone is
fully automatic and performed using a 3D active shape model based approach driven using gradient and texture
information. The 3D active shape model is automatically initialized using a robust affine registration. The
approach is validated using a database of 18 FLASH MR images that are manually segmented, with an average
segmentation overlap (Dice similarity coefficient) of 0.92 compared to 0.9 obtained using the classifier only.
Subdivision surfaces and parameterization are desirable for many algorithms that are commonly used in Medical Image Analysis. However, extracting an accurate surface and parameterization can be difficult for many anatomical objects of interest, due to noisy segmentations and the inherent variability of the object. The thin cartilages of the knee are an example of this, especially after damage is incurred from injuries or conditions like osteoarthritis. As a result, the cartilages can have different topologies or exist in multiple pieces. In this paper we present a topology preserving (genus 0) subdivision-based parametric deformable model that is used to extract the surfaces of the patella and tibial cartilages in the knee. These surfaces have minimal thickness in areas without cartilage. The algorithm inherently incorporates several desirable properties, including: shape based interpolation, sub-division remeshing and parameterization. To illustrate the usefulness of this approach, the surfaces and parameterizations of the patella cartilage are used to generate a 3D statistical shape model.
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