997 research outputs found
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
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 vessels’ 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
High rank tensor and spherical harmonic models for diffusion MRI processing
Diffusion tensor imaging (DTI) is a non-invasive quantitative method of characterizing tissue micro-structure. Diffusion imaging attempts to characterize the manner by which the water molecules within a particular location move within a given amount of time. Measurement of the diffusion tensor (D) within a voxel allows a macroscopic voxel-averaged description of fiber structure, orientation and fully quantitative evaluation of the microstructural features of healthy and diseased tissue.;The rank two tensor model is incapable of resolving multiple fiber orientations within an individual voxel. This shortcoming of single tensor model stems from the fact that the tensor possesses only a single orientational maximum. Several authors reported this non-mono-exponential behavior for the diffusion-induced attenuation in brain tissue in water and N-Acetyl Aspartate (NAA) signals, that is why the Multi-Tensor, Higher Rank Tensor and Orientation Distribution Function (ODF) were introduced.;Using the higher rank tensor, we will propose a scheme for tensor field interpolation which is inspired by subdivision surfaces in computer graphics. The method applies to Cartesian tensors of all ranks and imposes smoothness on the interpolated field by constraining the divergence and curl of the tensor field. Results demonstrate that the subdivision scheme can better preserve anisotropicity and interpolate rotations than some other interpolation methods. As one of the most important applications of DTI, fiber tractography was implemented to study the shape geometry changes. Based on the divergence and curl measurement, we will introduce new scalar measures that are sensitive to behaviors such as fiber bending and fanning.;Based on the ODF analysis, a new anisotropy measure that has the ability to describe multi-fiber heterogeneity while remaining rotationally invariant, will be introduced, which is a problem with many other anisotropy measures defined using the ODF. The performance of this novel measure is demonstrated for data with varying Signal to Noise Ratio (SNR), and different material characteristics
Left Ventricular Fluid Mechanics: the long way from theoretical models to clinical applications
\u2014The flow inside the left ventricle is characterized
by the formation of vortices that smoothly accompany blood
from the mitral inlet to the aortic outlet. Computational fluid
dynamics permitted to shed some light on the fundamental
processes involved with vortex motion. More recently,
patient-specific numerical simulations are becoming an
increasingly feasible tool that can be integrated with the
developing imaging technologies. The existing computational
methods are reviewed in the perspective of their potential role
as a novel aid for advanced clinical analysis. The current
results obtained by simulation methods either alone or in
combination with medical imaging are summarized. Open
problems are highlighted and perspective clinical applications
are discussed
Contributions of Continuous Max-Flow Theory to Medical Image Processing
Discrete graph cuts and continuous max-flow theory have created a paradigm shift in many areas of medical image processing. As previous methods limited themselves to analytically solvable optimization problems or guaranteed only local optimizability to increasingly complex and non-convex functionals, current methods based now rely on describing an optimization problem in a series of general yet simple functionals with a global, but non-analytic, solution algorithms. This has been increasingly spurred on by the availability of these general-purpose algorithms in an open-source context. Thus, graph-cuts and max-flow have changed every aspect of medical image processing from reconstruction to enhancement to segmentation and registration.
To wax philosophical, continuous max-flow theory in particular has the potential to bring a high degree of mathematical elegance to the field, bridging the conceptual gap between the discrete and continuous domains in which we describe different imaging problems, properties and processes. In Chapter 1, we use the notion of infinitely dense and infinitely densely connected graphs to transfer between the discrete and continuous domains, which has a certain sense of mathematical pedantry to it, but the resulting variational energy equations have a sense of elegance and charm. As any application of the principle of duality, the variational equations have an enigmatic side that can only be decoded with time and patience.
The goal of this thesis is to show the contributions of max-flow theory through image enhancement and segmentation, increasing incorporation of topological considerations and increasing the role played by user knowledge and interactivity. These methods will be rigorously grounded in calculus of variations, guaranteeing fuzzy optimality and providing multiple solution approaches to addressing each individual problem
Computational methods to predict and enhance decision-making with biomedical data.
The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed
Anisotropic EEG/MEG volume conductor modeling based on Diffusion Tensor Imaging
Die vorliegende Arbeit befasst sich mit der Volumenleitermodellierung auf
Basis der Finiten Elemente für EEG/MEG Untersuchungen unter Einbeziehung
von Anistropieinformation, die mit Hilfe der
Magnetresonanzdiffusionstensorbildgebung (MR-DTI) gewonnen wurde. Im ersten
Teil der Arbeit wurde der Einfluss unvollständig bestimmter
Wichtungsparamter (b-Matrix) auf die zu rekonstruierenden
Diffusionstensoren untersucht. Die Unvollständigkeit bezieht sich dabei auf
die Tatsache, dass im Allgemeinen nur die starken Diffusionsgradienten zur
Berechnung der b-Matrix herangezogen werden. Es wurde gezeigt, dass
besonders bei Aufnahmen mit hoher räumlicher Auflösung der Anteil der
Bildgradienten an der b-Matrix nicht mehr vernachlässigbar ist. Weiterhin
wurde gezeigt, wie man die b-Matrizen korrekt analytisch bestimmt und damit
einen systematischen Fehler vermeidet. Für den Fall, dass nicht ausreichend
Informationen zur Verfügung stehen um die analytische Bestimmung
durchzuführen, wurde eine Lösung vorgeschlagen, die es mit Hilfe von
Phantommessungen ermöglicht eine parametrisierte b-Matrix zu bestimmen. Der
zweite Teil widmet sich der Erstellung hochaufgelöster realistischer
Volumenleitermodelle detailliert beschrieben. Besonders die Transformation
der Diffusionstensordaten in Leitfähigkeitstensoren. Zudem wurde eine
Vorgehensweise beschrieben, die es erlaubt, einen T1-gewichteten
MR-Datensatz vollautomatisch in fünf verschiedene Gewebesegmente (weiches
Gewebe, graue und weiße Substanz, CSF und Schädelknochen) zu unterteilen.
Der dritte Teil der Arbeit befasst sich mit dem Einfluss der anisotropen
Leitfähigkeit in der weißen Hirnsubstanz auf EEG und MEG unter Verwendung
eines Tier- sowie eines Humanmodells. Um den Einfluss der verschiedenen
Methoden der Transformation von DTI Daten in Leitfähigkeitsdaten zu
untersuchen, wurden verschiedenen Modelle sowohl mit gemessener als auch
mit künstlicher Anisotropie erstellt. In der Tiermodellstudie wurden EEG
und in der Humanmodellstudie EEG und MEG Simulationen sowohl mit den
anisotropen Modellen als auch mit einem isotropen Modell durchgeführt und
miteinander verglichen. Dabei wurde gefunden, dass sowohl der
topographische Fehler (RDM) als auch der Magnitudenfehler stark durch das
Einbeziehen von Anisotropieinformationen beeinflusst wird. Es wurde auch
gezeigt, dass sowohl die Position als auch die Orientierung einer
dipolaren Quelle in Bezug auf das anisotrope Segment einen großen Effekt
auf die untersuchten Fehlermaße hat.In this work anisotropic electric tissue properties determined by
means of
diffusion tensor imaging were modeled into high resolution finite element
volume conductors. In first part of the work the influence of not
considering imaging gradient in the calculation of the b-matrices on the
correct determination of diffusion tensor data is shown and it was found
that especially with high resolution imaging protocols the contributions of
the imaging gradients is not negligible. It was also shown how correct
b-matrices considering all applied gradients can be calculated correctly.
For the case that information about the sequence are missing an
experimental approach of determining a parameterized b-matrix using phantom
measurements is proposed. In the second part the procedure of generating
anisotropic volume conductor models is regarded. The main focus of this
part was to facilitate the derivation of anisotropy information from DTI
measurements and the inclusion of this information into an anisotropic
volume conductor. It was shown, that it is possible to generate a
sophisticated high resolution anisotropic model without any manual steps
into five different tissue layers. The third part studied the influence of
anisotropic white matter employing an animal as well as a human model. To
compare the different ways of converting the anisotropy information from
DTI into conductivity information, different models were investigated,
having artificial as well as measured anisotropy. In the animal study the
EEG and in the human study the EEG and MEG forward solution was studies
using the anisotropic models and compared to the solution derived using an
isotropic model. It was found that both, the topography error (RDM) as well
as the magnitude error (MAG), are significantly affected if anisotropy is
considered in the volume conductor. It was also shown, that the position as
well as the orientation of the dipole with respect to white matter has a
large effect on the amount of the error quantities. Finally, it is claimed
that if one uses high resolution volume conductor models for EEG/MEG
studies, the anisotropy has to be considered, since the average error of
neglecting anisotropy is larger than the accuracy which can be achieved
using such models
Segmentation of neuroanatomy in magnetic resonance images
Segmentation in neurological Magnetic Resonance Imaging (MRI) is necessary for volume measurement, feature extraction and for the three-dimensional display of neuroanatomy. This thesis proposes several automated and semi-automated methods which offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. Work has concentrated on the use of dual echo multi-slice spin-echo data sets in order to take advantage of the intrinsically multi-parametric nature of MRI. Such data is widely acquired clinically and segmentation therefore does not require additional scans. The literature has been reviewed. Factors affecting image non-uniformity for a modem 1.5 Tesla imager have been investigated. These investigations demonstrate that a robust, fast, automatic three-dimensional non-uniformity correction may be applied to data as a pre-processing step. The merit of using an anisotropic smoothing method for noisy data has been demonstrated. Several approaches to neurological MRI segmentation have been developed. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing, two threshold based techniques and a fast radial CSF identification approach are proposed to identify the intracranial region contour in each slice of the data set. Once isolated, the intracranial region is further processed to identify CSF, and, depending upon the MRI pulse sequence used, the brain itself may be sub-divided into grey matter and white matter using semiautomatic contrast enhancement and clustering methods. The segmentation of Multiple Sclerosis (MS) plaques has also been considered. The utility of the stack, a data driven multi-resolution approach to segmentation, has been investigated, and several improvements to the method suggested. The factors affecting the intrinsic accuracy of neurological volume measurement in MRI have been studied and their magnitudes determined for spin-echo imaging. Geometric distortion - both object dependent and object independent - has been considered, as well as slice warp, slice profile, slice position and the partial volume effect. Finally, the accuracy of the approaches to segmentation developed in this thesis have been evaluated. Intracranial volume measurements are within 5% of expert observers' measurements, white matter volumes within 10%, and CSF volumes consistently lower than the expert observers' measurements due to the observers' inability to take the partial volume effect into account
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