4,597 research outputs found
A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images
Template estimation plays a crucial role in computational anatomy since it
provides reference frames for performing statistical analysis of the underlying
anatomical population variability. While building models for template
estimation, variability in sites and image acquisition protocols need to be
accounted for. To account for such variability, we propose a generative
template estimation model that makes simultaneous inference of both bias fields
in individual images, deformations for image registration, and variance
hyperparameters. In contrast, existing maximum a posterori based methods need
to rely on either bias-invariant similarity measures or robust image
normalization. Results on synthetic and real brain MRI images demonstrate the
capability of the model to capture heterogeneity in intensities and provide a
reliable template estimation from registration
Most Likely Separation of Intensity and Warping Effects in Image Registration
This paper introduces a class of mixed-effects models for joint modeling of
spatially correlated intensity variation and warping variation in 2D images.
Spatially correlated intensity variation and warp variation are modeled as
random effects, resulting in a nonlinear mixed-effects model that enables
simultaneous estimation of template and model parameters by optimization of the
likelihood function. We propose an algorithm for fitting the model which
alternates estimation of variance parameters and image registration. This
approach avoids the potential estimation bias in the template estimate that
arises when treating registration as a preprocessing step. We apply the model
to datasets of facial images and 2D brain magnetic resonance images to
illustrate the simultaneous estimation and prediction of intensity and warp
effects
Segmentation of brain MRI during early childhood
The objective of this thesis is the development of automatic methods to measure the changes in
volume and growth of brain structures in prematurely born infants. Automatic tools for accurate
tissue quantification from magnetic resonance images can provide means for understanding
how the neurodevelopmental effects of the premature birth, such as cognitive, neurological or
behavioural impairment, are related to underlying changes in brain anatomy. Understanding
these changes forms a basis for development of suitable treatments to improve the outcomes of
premature birth.
In this thesis we focus on the segmentation of brain structures from magnetic resonance images
during early childhood. Most of the current brain segmentation techniques have been focused
on the segmentation of adult or neonatal brains. As a result of rapid development, the brain
anatomy during early childhood differs from anatomy of both adult and neonatal brains and
therefore requires adaptations of available techniques to produce good results.
To address the issue of anatomical differences of the brain during early childhood compared
to other age-groups, population-specific deformable and probabilistic atlases are introduced. A
method for generation of population-specific prior information in form of a probabilistic atlas
is proposed and used to enhance existing segmentation algorithms.
The evaluation of registration-based and intensity-based approaches shows the techniques to
be complementary in the quality of automatic segmentation in different parts of the brain. We
propose a novel robust segmentation method combining the advantages of both approaches. The
method is based on multiple label propagation using B-spline non-rigid registration followed by
EM segmentation.
Intensity inhomogeneity is a shading artefact resulting from the acquisition process, which
significantly affects modern high resolution MR data acquired at higher magnetic field strengths.
A novel template based method focused on correcting the intensity inhomogeneity in data
acquired at higher magnetic field strengths is therefore proposed.
The proposed segmentation method combined with proposed intensity inhomogeneity correction
method offers a robust tool for quantification of volumes and growth of brain structures during
early childhood. The tool have been applied to 67 T1-weigted images of subject at one and two years of age
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.
OBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD
MR-based attenuation correction and scatter correction in neurological PET/MR imaging with 18F-FDG
The aim was to investigate the effects of MR-based attenuation correction (MRAC) and scatter correction to positron emission tomography (PET) image quantification in neurological PET/MR with 18F-FDG. A multi-center phantom study was conducted to investigate the effect of MRAC between PET/MR and PET/CT systems (I). An MRAC method to derive bone from T1-weighted MR images was developed (II, III). Finally, scatter correction accuracy with MRAC was investigated (IV).
The results show that the quantitative accuracy in PET is well-comparable be-tween PET/MR and PET/CT systems when an attenuation correction method resembling CT-based attenuation correction (CTAC) is implemented. This al-lows achieving of a PET bias within standard uptake value (SUV) quantification repeatability (< 10 % error) and is within the repeatability of PET in most sys-tems and brain regions (< 5 % error). In addition, MRAC considering soft tissue, air and bone can be derived using T1-weighted images alone. The improved version of the MRAC method allows achieving a quantitative accuracy feasible for advanced applications (< 5 % error). MRAC has a minor effect on the scatter correction accuracy (< 3 % error), even when using MRAC without bone.
In conclusion, MRAC can be considered the largest contributing factor to PET quantification bias in 18F-FDG neurological PET/MR. This finding is not explicitly limited only to 18F-FDG imaging. Once an MRAC method that performs close to CTAC is implemented, there is no reason why a PET/MR system would perform differently from a PET/CT system. Such an MRAC method has been developed and is freely available (http://bit.ly/2fx6Jjz). Scatter correction can be considered a non-issue in neurological PET/MR imaging when using 18F-FD
Automated template-based brain localization and extraction for fetal brain MRI reconstruction.
Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5%. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks
Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images
We present a novel kernel regression framework for smoothing scalar surface
data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel
constructed from the eigenfunctions, we formulate a new bivariate kernel
regression framework as a weighted eigenfunction expansion with the heat kernel
as the weights. The new kernel regression is mathematically equivalent to
isotropic heat diffusion, kernel smoothing and recently popular diffusion
wavelets. Unlike many previous partial differential equation based approaches
involving diffusion, our approach represents the solution of diffusion
analytically, reducing numerical inaccuracy and slow convergence. The numerical
implementation is validated on a unit sphere using spherical harmonics. As an
illustration, we have applied the method in characterizing the localized growth
pattern of mandible surfaces obtained in CT images from subjects between ages 0
and 20 years by regressing the length of displacement vectors with respect to
the template surface.Comment: Accepted in Medical Image Analysi
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