205 research outputs found
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
Coronary Artery Segmentation and Motion Modelling
Conventional coronary artery bypass surgery requires invasive sternotomy and the
use of a cardiopulmonary bypass, which leads to long recovery period and has high
infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery
based on image guided robotic surgical approaches have been developed to allow the
clinicians to conduct the bypass surgery off-pump with only three pin holes incisions
in the chest cavity, through which two robotic arms and one stereo endoscopic camera
are inserted. However, the restricted field of view of the stereo endoscopic images leads
to possible vessel misidentification and coronary artery mis-localization. This results
in 20-30% conversion rates from TECAB surgery to the conventional approach.
We have constructed patient-specific 3D + time coronary artery and left ventricle
motion models from preoperative 4D Computed Tomography Angiography (CTA)
scans. Through temporally and spatially aligning this model with the intraoperative
endoscopic views of the patient's beating heart, this work assists the surgeon to identify
and locate the correct coronaries during the TECAB precedures. Thus this work has
the prospect of reducing the conversion rate from TECAB to conventional coronary
bypass procedures.
This thesis mainly focus on designing segmentation and motion tracking methods
of the coronary arteries in order to build pre-operative patient-specific motion models.
Various vessel centreline extraction and lumen segmentation algorithms are presented,
including intensity based approaches, geometric model matching method and
morphology-based method. A probabilistic atlas of the coronary arteries is formed
from a group of subjects to facilitate the vascular segmentation and registration procedures.
Non-rigid registration framework based on a free-form deformation model
and multi-level multi-channel large deformation diffeomorphic metric mapping are
proposed to track the coronary motion. The methods are applied to 4D CTA images
acquired from various groups of patients and quantitatively evaluated
Vessel tractography using an intensity based tensor model
In the last decade, CAD (Coronary Artery Disease) has been the leading cause of death worldwide [1]. Extraction of arteries is a crucial step for accurate visualization, quantification, and tracking of pathologies. However, coronary artery segmentation is one of the most challenging problems in medical image analysis, since arteries are complex tubular structures with bifurcations, and have possible pathologies. Moreover, appearance of blood vessels and their geometry can be perturbed by stents, calcifications and pathologies such as stenosis. Besides, noise, contrast and resolution artifacts can make the problem more challenging. In this thesis, we present a novel tubular structure segmentation method based on an intensity-based tensor that fits to a vessel, which is inspired from diffusion tensor image (DTI) modeling. The anisotropic tensor inside the vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized with a single seed point and it is capable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrate the performance of our algorithm on 3 complex tubular structured synthetic datasets, and on 8 CTA (Computed Tomography Angiography) datasets (from Rotterdam Coronary Artery Algorithm Evaluation Framework) for quantitative validation. Additionally, extracted arteries from 10 CTA volumes are qualitatively evaluated by a cardiologist expert's visual scores
Incorporating the Aortic Valve into Computational Fluid Dynamics Models using Phase-Contrast MRI and Valve Tracking
The American Heart Association states about 2% of the general population have a bicuspid aortic valve (BAV). BAVs exist in 80% of patients with aortic coarctation (CoA) and likely influences flow patterns that contribute to long-term morbidity post-surgically. BAV patients tend to have larger ascending aortic diameters, increased risk of aneurysm formation, and require surgical intervention earlier than patients with a normal aortic valve. Magnetic resonance imaging (MRI) has been used clinically to assess aortic arch morphology and blood flow in these patients. These MRI data have been used in computational fluid dynamics (CFD) studies to investigate potential adverse hemodynamics in these patients, yet few studies have attempted to characterize the impact of the aortic valve on ascending aortic hemodynamics.
To address this issue, this research sought to identify the impact of aortic valve morphology on hemodynamics in the ascending aorta and determine the location where the influence is negligible. Novel tools were developed to implement aortic valve morphology into CFD models and compensate for heart motion in MRI flow measurements acquired through the aortic valve. Hemodynamic metrics such as blood flow velocity, time-averaged wall shear stress (TAWSS), and turbulent kinetic energy (TKE) induced by the valve were compared to values obtained using the current plug inflow approach. The influence of heart motion on these metrics was also investigated, resulting in the underestimation of TAWSS and TKE when heart motion was neglected. CFD simulations of CoA patients exhibiting bicuspid and tricuspid aortic valves were performed in models including the aortic sinuses and patient-specific valves. Results indicated the aortic valve impacted hemodynamics primarily in the ascending aorta, with the BAV having the greatest influence along the outer right wall of the vessel. A marked increase in TKE is present in aortic valve simulations, particularly in BAV patients. These findings suggest that future CFD studies investigating altered hemodynamics in the ascending aorta should accurately replicate aortic valve morphology. Further, aortic valve disease impacts hemodynamics in the ascending aorta that may be a predictor of the development or progression of ascending aortic dilation and possible aneurysm formation in this region
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