228 research outputs found
Segmentation-based blood flow parameter refinement in cerebrovascular structures using 4D arterial spin labeling MRA
Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and blood flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses blood flow data to improve its accuracy, is extended to estimate blood flow parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated blood flow parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined blood flow estimation is more realistic compared to the raw hemodynamic parameters. Conclusion: The proposed method can provide accurate segmentations and blood flow parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively
From the macro- to the microvasculature : temporal and spatial visualization using arterial spin labeling
For many cerebrovascular
diseases, visualization of blood flow through the large vasculature, as well as
quantitative information on tissue perfusion, is very important. Arterial Spin
labelling (ASL) magnetic resonance (MR) imaging enables the visualization of
arterial flow by labelling the magnetization of arterial blood using
radiofrequency pulses. The labelled arterial blood acts as an endogenous tracer
and allows, which can avoid the reliance on the use of contrast agents.
In this doctoral thesis, several new techniques for dynamic MR angiography and
perfusion imaging were developed based on ASL techniques, which include pulsed
ASL, pseudo-continuous ASL (pCASL), vessel-encoded pCASL, time-encoded pCASL as
well as simultaneous multi-slice pCASL. The underlying motivation of these
development is to reduce the burden on patients by employing non-invasive ASL
techniques as potential alternatives to X-ray digital subtraction angiography,
contrast-enhanced MR angiography and perfusion imaging. In each study, the
optimum ASL techniques was carefully chosen by considering the pros and cons of
the technique to achieve better clinical usability, while improving robustness
against potential artifacts.LUMC / Geneeskund
MRI Visualization of Whole Brain Macro- and Microvascular Remodeling in a Rat Model of Ischemic Stroke: A Pilot Study
Using superparamagnetic iron oxide nanoparticles (SPION) as a single contrast agent, we investigated dual contrast cerebrovascular magnetic resonance imaging (MRI) for simultaneously monitoring macro- and microvasculature and their association with ischemic edema status (via apparent diffusion coefficient [ADC]) in transient middle cerebral artery occlusion (tMCAO) rat models. High-resolution T1-contrast based ultra-short echo time MR angiography (UTE-MRA) visualized size remodeling of pial arteries and veins whose mutual association with cortical ischemic edema status is rarely reported. ??R2?????R2*-MRI-derived vessel size index (VSI) and density indices (Q and MVD) mapped morphological changes of microvessels occurring in subcortical ischemic edema lesions. In cortical ischemic edema lesions, significantly dilated pial veins (p???=???0.0051) and thinned pial arteries (p???=???0.0096) of ipsilateral brains compared to those of contralateral brains were observed from UTE-MRAs. In subcortical regions, ischemic edema lesions had a significantly decreased Q and MVD values (p???<???0.001), as well as increased VSI values (p???<???0.001) than normal subcortical tissues in contralateral brains. This pilot study suggests that MR-based morphological vessel changes, including but not limited to venous blood vessels, are directly related to corresponding tissue edema status in ischemic stroke rat models
Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction
A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vesselsâ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives
Various structures in human physiology follow a treelike morphology, which
often expresses complexity at very fine scales. Examples of such structures are
intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large
collections of 2D and 3D images have been made available by medical imaging
modalities such as magnetic resonance imaging (MRI), computed tomography (CT),
Optical coherence tomography (OCT) and ultrasound in which the spatial
arrangement can be observed. Segmentation of these structures in medical
imaging is of great importance since the analysis of the structure provides
insights into disease diagnosis, treatment planning, and prognosis. Manually
labelling extensive data by radiologists is often time-consuming and
error-prone. As a result, automated or semi-automated computational models have
become a popular research field of medical imaging in the past two decades, and
many have been developed to date. In this survey, we aim to provide a
comprehensive review of currently publicly available datasets, segmentation
algorithms, and evaluation metrics. In addition, current challenges and future
research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa
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
Modeling and hexahedral meshing of cerebral arterial networks from centerlines
Computational fluid dynamics (CFD) simulation provides valuable information
on blood flow from the vascular geometry. However, it requires extracting
precise models of arteries from low-resolution medical images, which remains
challenging. Centerline-based representation is widely used to model large
vascular networks with small vessels, as it encodes both the geometric and
topological information and facilitates manual editing. In this work, we
propose an automatic method to generate a structured hexahedral mesh suitable
for CFD directly from centerlines. We addressed both the modeling and meshing
tasks. We proposed a vessel model based on penalized splines to overcome the
limitations inherent to the centerline representation, such as noise and
sparsity. The bifurcations are reconstructed using a parametric model based on
the anatomy that we extended to planar n-furcations. Finally, we developed a
method to produce a volume mesh with structured, hexahedral, and flow-oriented
cells from the proposed vascular network model. The proposed method offers
better robustness to the common defects of centerlines and increases the mesh
quality compared to state-of-the-art methods. As it relies on centerlines
alone, it can be applied to edit the vascular model effortlessly to study the
impact of vascular geometry and topology on hemodynamics. We demonstrate the
efficiency of our method by entirely meshing a dataset of 60 cerebral vascular
networks. 92% of the vessels and 83% of the bifurcations were meshed without
defects needing manual intervention, despite the challenging aspect of the
input data. The source code is released publicly
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