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Multi-scale parameterisation of static and dynamic continuum porous perfusion models using discrete anatomical dataHyde, Eoin Ronan January 2014 (has links)
The aim of this thesis is to replace the intractable problem of using discrete flow models within large vascular networks with a suitably parameterised and tractable continuum perfusion model. Through this work, we directly address the hypothesis that discrete vascular data can be incorporated within continuum perfusion models via spatially-averaged parameterisation techniques. Chapter 1 reviews biological perfusion from both clinical and computational modelling perspectives, with a particular focus on myocardial perfusion. In Chapter 2, a synthetic 3D vascular network was constructed, which was controllable in terms of its size and properties. A multi-compartment static Darcy perfusion model of this discrete system was parameterised via a number of techniques. Permeabilities were derived using: (i) porosity-scaled isotropic (ϕI); (ii) Huyghe and Van Campen (HvC); and (iii) projected-PCA parameterisation methods. It was found that HvC permeabilities and pressure-coupling fields derived from the discrete data produced the best comparison to the spatially-averaged Poiseuille pressure. In Chapter 3, the construction and analysis of high-resolution anatomical arterial vascular models was undertaken. In Chapter 4, various anatomically-derived vascular networks were used to parameterise our perfusion model, including a microCT-derived rat capillary network, a single arterial subtree, and canine and porcine whole-organ arterial models. Allowing for general-connectivity (as opposed to strictly-hierarchical connectivity) yielded a significant improvement on the continuum model pressure. For the whole-organ model however, it was found that the best results were obtained by using porosity-scaled isotropic permeabilities and anatomically-derived pressure-coupling fields. It was also discovered that naturally occurring small length but relatively large radius vessels were not suitable for the HvC method. In Chapter 5, the suitability of derived parameters for use within a dynamic perfusion model was examined. It was found that the parameters derived from the original static network were adequate for application throughout the cardiac cycle. Chapter 6 presents a concluding discussion, highlighting limitations and future directions to be investigated.
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Patient-specific prospective respiratory motion correction in cardiovascular MRI.Bush, Michael 29 August 2019 (has links)
No description available.
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Novel Techniques for Rapid Cardiac Perfusion Magnetic Resonance Imaging with Whole Heart CoverageWang, Haonan 01 June 2016 (has links)
Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging method that is used in the diagnosis of many common diseases. Compared to other medical imaging modalities, MRI has the ability to provide high-resolution 2D and 3D images in arbitrary orientations, without the use of potentially damaging ionizing radiation. Myocardial perfusion MRI is a promising non- invasive clinical way to detect cardiac disease. It can also provide quantitative analysis for blood flow within the heart. However, MRI requires longer scan times to acquire images at comparable resolutions to some other imaging modalities. Increasing image resolution, both spatially and temporally, is very important to myocardial perfusion MRI. The work presented in this dissertation focuses on the development of novel dynamic contrast-enhanced (DCE) MRI that is able to achieve both high spatial and temporal resolutions, as well as suitable spatial coverage of the heart. Three novel acquisition and reconstruction frame- works are proposed and analyzed in this dissertation. The first framework we propose uses a highly undersampled 3D Cartesian acquisition and total variation (TV) constrained reconstruction to accelerate the acquisition of myocardial perfu- sion images. This technique increases temporal resolution for contrast tracking without sacrificing spatial resolution. An analysis of the effect of different k-space trajectories using this technique is performed. The purpose of the second framework is to simplify cardiac perfusion studies. An ECG- gated saturation recovery sequence is regularly used for cardiac perfusion imaging. However, using an ungated acquisition has the potential benefit of reducing the acquisition time by eliminating the need for the ECG trigger signal. We present a novel non-Cartesian 2D multi-slice ungated acquisition, and demonstrate that it is a promising alternative to ECG-gated cardiac perfusion studies. An optimization analysis of our ungated acquisition is also presented. The third method in this dissertation combines the 2D ungated acquisition with multi-band excitation, which enables the excitation of multiple slices simultaneously. This method is able to reduce scan time not only through the ungated acquisition, but also from obtaining multiple slices at once. This allows us to achieve whole heart coverage without sacrificing temporal resolution. The contributions presented in this dissertation demonstrate the basic feasibility of car- diac perfusion MRI achieving whole-heart coverage in a clinical setting by overcoming the major existing limitations: speed of acquisition and spatial coverage.
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Approches variationnelles statistiques spatio-temporelles pour l'analyse quantitative de la perfusion myocardique en IRM / Spatio-temporal statistical variational models for the quantitative assessment of myocardial perfusion in magnetic resonance imagingHamrouni-Chtourou, Sameh 11 July 2012 (has links)
L'analyse quantitative de la perfusion myocardique, i.e. l'estimation d'indices de perfusion segmentaires puis leur confrontation à des valeurs normatives, constitue un enjeu majeur pour le dépistage, le traitement et le suivi des cardiomyopathies ischémiques --parmi les premières causes de mortalité dans les pays occidentaux. Dans la dernière décennie, l'imagerie par résonance magnétique de perfusion (IRM-p) est la modalité privilégiée pour l'exploration dynamique non-invasive de la perfusion cardiaque. L'IRM-p consiste à acquérir des séries temporelles d'images cardiaques en incidence petit-axe et à plusieurs niveaux de coupe le long du grand axe du cœur durant le transit d'un agent de contraste vasculaire dans les cavités et le muscle cardiaques. Les examens IRM-p résultants présentent de fortes variations non linéaires de contraste et des artefacts de mouvements cardio-respiratoires. Dans ces conditions, l'analyse quantitative de la perfusion myocardique est confrontée aux problèmes complexes de recalage et de segmentation de structures cardiaques non rigides dans des examens IRM-p. Cette thèse se propose d'automatiser l’analyse quantitative de la perfusion du myocarde en développant un outil d'aide au diagnostic non supervisé dédié à l'IRM de perfusion cardiaque de premier passage, comprenant quatre étapes de traitement : -1.sélection automatique d'une région d'intérêt centrée sur le cœur; -2.compensation non rigide des mouvements cardio-respiratoires sur l'intégralité de l'examen traité; -3.segmentation des contours cardiaques; -4.quantification de la perfusion myocardique. Les réponses que nous apportons aux différents défis identifiés dans chaque étape s'articulent autour d'une idée commune : exploiter l'information liée à la cinématique de transit de l'agent de contraste dans les tissus pour discriminer les structures anatomiques et guider le processus de recalage des données. Ce dernier constitue le travail central de cette thèse. Les méthodes de recalage non rigide d'images fondées sur l'optimisation de mesures d'information constituent une référence en imagerie médicale. Leur cadre d'application usuel est l'alignement de paires d'images par appariement statistique de distributions de luminance, manipulées via leurs densités de probabilité marginales et conjointes, estimées par des méthodes à noyaux. Efficaces pour des densités jointes présentant des classes individualisées ou réductibles à des mélanges simples, ces approches atteignent leurs limites pour des mélanges non-linéaires où la luminance au pixel s’avère être un attribut trop frustre pour permettre une décision statistique discriminante, et pour des données mono-modal avec variations non linéaires et multi-modal. Cette thèse introduit un modèle mathématique de recalage informationnel multi-attributs/multi-vues générique répondant aux défis identifiés: (i) alignement simultané de l'intégralité de l'examen IRM-p analysé par usage d'un atlas, naturel ou synthétique, dans lequel le cœur est immobile et en utilisant les courbes de rehaussement au pixel comme ensemble dense de primitives; et (ii) capacité à intégrer des primitives image composites, spatiales ou spatio-temporelles, de grande dimension. Ce modèle, disponible dans le cadre classique de Shannon et dans le cadre généralisé d'Ali-Silvey, est fondé sur de nouveaux estimateurs géométriques de type k plus proches voisins des mesures d'information, consistants en dimension arbitraire. Nous étudions leur optimisation variationnelle en dérivant des expressions analytiques de leurs gradients sur des espaces de transformations spatiales régulières de dimension finie et infinie, et en proposant des schémas numériques et algorithmiques de descente en gradient efficace. Ce modèle de portée générale est ensuite instancié au cadre médical ciblé, et ses performances, notamment en terme de précision et de robustesse, sont évaluées dans le cadre d'un protocole expérimental tant qualitatif que quantitatif / Quantitative assessment of moycardium perfusion, i.e. computation of perfusion parameters which are then confronted to normative values, is a key issue for the diagnosis, therapy planning and monitoring of ischemic cardiomyopathies --the leading cause of death in Western countries. Within the last decade, perfusion magnetic resonance imaging (p-MRI) has emerged as a reference modality for reliably assessing myocardial perfusion in a noninvasive and accurate way. In p-MRI acquisitions, short-axis image sequences are captured at multiple slice levels along the long-axis of the heart during the transit of a vascular contrast agent through the cardiac chambers and muscle. Resulting p-MRI exams exhibit high nonlinear contrast variations and complex cardio-thoracic motions. Perfusion assessment is then faced with the complex problems of non rigid registration and segmentation of cardiac structures in p-MRI exams. The objective of this thesis is enabling an automated quantitative computer-aided diagnosis tool for first pass cardiac perfusion MRI, comprising four processing steps: -1.automated cardiac region of interest extraction; -2.non rigid registration of cardio-thoracic motions throughout the whole sequence; -3.cardiac boundaries segmentation; -4.quantification of myocardial perfusion. The answers we give to the various challenges identified in each step are based on a common idea: investigating information related to the kinematics of contrast agent transit in the tissues for discriminating the anatomical structures and driving the alignment process. This latter is the main work of this thesis. Non rigid image registration methods based on the optimization of information measures provide versatile solutions for robustly aligning medical data. Their usual application setting is the alignment of image pairs by statistically matching luminance distributions, handled using marginal and joint probability densities estimated via kernel techniques. Though efficient for joint densities exhibiting well-separated clusters or reducible to simple mixtures, these approaches reach their limits for nonlinear mixtures where pixelwise luminance appears to be a too coarse feature for allowing unambiguous statistical decisions, and for mono-modal with nonlinear variations and multi-modal data. This thesis presents a unified mathematical model for the information-theoretic multi-feature/multi-view non rigid registration, addressing the identified challenges : (i) simultaneous registration of the whole p-MRI exam, using a natural or synthetic atlas generated as a motion-free exam depicting the transit of the vascular contrast agent through cardiac structures and using local contrast enhancement curves as a feature set; (ii) can be easily generalized to richer feature spaces combining radiometric and geometric information. The resulting model is based on novel consistent k-nearest neighbors estimators of information measures in high dimension, for both classical Shannon and generalized Ali-Silvey frameworks. We study their variational optimization by deriving under closed-form their gradient flows over finite and infinite dimensional smooth transform spaces, and by proposing computationally efficient gradient descent schemas. The resulting generic theoretical framework is applied to the groupwise alignment of cardiac p-MRI exams, and its performances, in terms of accuracy and robustness, are evaluated in an experimental qualitative and quantitative protocol
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