55 research outputs found
Three-dimensional model-based analysis of vascular and cardiac images
This thesis is concerned with the geometrical modeling of organs to perform medical image analysis tasks. The
thesis is divided in two main parts devoted to model linear vessel segments and the left ventricle of the heart,
respectively. Chapters 2 to 4 present different aspects of a model-based technique for semi-automated
quantification of linear vessel segments from 3-D Magnetic Resonance Angiography (MRA). Chapter 2 is
concerned with a multiscale filter for the enhancement of vessels in 2-D and 3-D angiograms. Chapter 3 applies
the filter developed in Chapter 2 to determine the central vessel axis in 3-D MRA images. This procedure is
initialized using an efficient user interaction technique that naturally incorporates the knowledge of the operator
about the vessel of interest. Also in this chapter, a linear vessel model is used to recover the position of the vessel
wall in order to carry out an accurate quantitative analysis of vascular morphology. Prior knowledge is provided
in two main forms: a cylindrical model introduces a shape prior while prior knowledge on the image acquisition
(type of MRA technique) is used to define an appropriate vessel boundary criterion. In Chapter 4 an extensive in
vitro and in vivo evaluation of the algorithm introduced in Chapter 3 is described. Chapters 5 to 7 change the
focus to 3D cardiac image analysis from Magnetic Resonance Imaging. Chapter 5 presents an extensive survey,
a categorization and a critical review of the field of cardiac modeling. Chapter 6 and Chapter 7 present
successive refinements of a method for building statistical models of shape variability with particular emphasis on
cardiac modeling. The method is based on an elastic registration method using hierarchical free-form
deformations. A 3D shape model of the left and right ventricles of the heart was constructed. This model
contains both the average shape of these organs as well as their shape variability. The methodology presented in
the last two chapters could also be applied to other anatomical structures. This has been illustrated in Chapter 6
with examples of geometrical models of the nucleus caudate and the radius
Development of a Heart Motion Tracking System using Non-invasive Imaging Data
Cardiac motion can be monitored non-invasively for the assessment of cardiovascular function by using medical imaging systems and motion tracking algorithms. Existing tracking approaches require a priori understanding of the non-rigid motion of the target system, which could change over multiple cardiac cycles and lead to tracking failures. The purpose of this research is to develop the algorithm and software, with computer vision techniques, to continuously track the motion of a user-defined region of the heart images. The proposed algorithm improves upon existing techniques because it does not require an underlying motion model, it quantifies the quality of tracking, and it can recover from a failed tracking estimate. The motion estimation of a non-rigid system will be done by a piecewise tracking approach that breaks up the region of interest into several small segments (patches), which can be approximated with interconnected pseudo-rigid segments. These segments will be initialized based on two criteria: 1) motion within a segment must follow the pseudo-rigid body model; and 2) motion in neighboring segments must be similar to each other. Segments are subsequently tracked as pseudo-rigid bodies, and the criteria described above are also used to detect failures in tracking. If a failure were to occur, the tracking algorithm will be reinitialized automatically. This algorithm was shown to be accurate and efficient, and has been tested on several heart motion data sets
Cardiac Imaging for Regenerative Therapy and Tissue Engineering
Cardiovascular disease remains the number 1 cause of death worldwide. Over the past 20 years, therapies for treating cardiac disease have come of age and coronary heart disease in particular has seen a revolution in new treatments such as statins, stents and beta blockers. These therapies have slowed death rates and have shown potential to minimise ischemia induced atrophy following myocardial infarction. Crucially however, they are unable to recover lost heart function due to cardiomyocyte death, resulting in poor prognosis for patients. Myocardial regeneration therapy is a new strategy towards treating cardiac disease that engrafts regenerative cells and biomaterials to the myocardium to stimulate repair of tissue and restore contractile function. Cardiac regeneration therapy has made a rapid translation from preclinical research to clinical trials with the first trial in humans published in 2001. Clinical trials in the years since however have produced underwhelming results and there is a general consensus that further preclinical optimisation with powerful non-invasive imaging data will be key to the future success of regenerative medicine in humans. Magnetic resonance imaging is unparalleled in providing non-invasive multiparametric imaging of both global and regional cardiac structure and function. MRI provides high spatiotemporal resolution and multiple contrast mechanisms revealing information about molecular changes in the myocardium. These imaging abilities make MRI a versatile and powerful tool in the preclinical optimisation of cardiac regeneration therapies. Over the chapters presented in this thesis I have established a set of MR imaging techniques that enable valuable in vivo characterisation of cardiac function and structure in for use in studies of regenerative therapy. It is hoped that the methods developed over the course of this thesis aid in the uptake of imaging applications in studies of regenerative medicine and that the wide range of imaging tools demonstrated help to bring regenerative medicine closer to practical clinical therapy
Recent Advances in Signal Processing
The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity
Machine learning approaches to model cardiac shape in large-scale imaging studies
Recent improvements in non-invasive imaging, together with the introduction of fully-automated segmentation algorithms and big data analytics, has paved the way for large-scale population-based imaging studies. These studies promise to increase our understanding of a large number of medical conditions, including cardiovascular diseases. However, analysis of cardiac shape in such studies is often limited to simple morphometric indices, ignoring large part of the information available in medical images. Discovery of new biomarkers by machine learning has recently gained traction, but often lacks interpretability. The research presented in this thesis aimed at developing novel explainable machine learning and computational methods capable of better summarizing shape variability, to better inform association and predictive clinical models in large-scale imaging studies.
A powerful and flexible framework to model the relationship between three-dimensional (3D) cardiac atlases, encoding multiple phenotypic traits, and genetic variables is first presented. The proposed approach enables the detection of regional phenotype-genotype associations that would be otherwise neglected by conventional association analysis. Three learning-based systems based on deep generative models are then proposed. In the first model, I propose a classifier of cardiac shapes which exploits task-specific generative shape features, and it is designed to enable the visualisation of the anatomical effect these features encode in 3D, making the classification task transparent. The second approach models a database of anatomical shapes via a hierarchy of conditional latent variables and it is capable of detecting, quantifying and visualising onto a template shape the most discriminative anatomical features that characterize distinct clinical conditions. Finally, a preliminary analysis of a deep learning system capable of reconstructing 3D high-resolution cardiac segmentations from a sparse set of 2D views segmentations is reported. This thesis demonstrates that machine learning approaches can facilitate high-throughput analysis of normal and pathological anatomy and of its determinants without losing clinical interpretability.Open Acces
Advancements and Breakthroughs in Ultrasound Imaging
Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world
Preclinical MRI of the Kidney
This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers
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
Molecular Imaging
The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world
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