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Author: Belén Casas Garcia
Publisher: Linköping University Electronic Press
ISBN: 9176852172
Category :
Languages : en
Pages : 71
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Book Description
Current diagnostic tools for assessing cardiovascular disease mostly focus on measuring a given biomarker at a specific spatial location where an abnormality is suspected. However, as a result of the dynamic and complex nature of the cardiovascular system, the analysis of isolated biomarkers is generally not sufficient to characterize the pathological mechanisms behind a disease. Model-based approaches that integrate the mechanisms through which different components interact, and present possibilities for system-level analyses, give us a better picture of a patient’s overall health status. One of the main goals of cardiovascular modelling is the development of personalized models based on clinical measurements. Recent years have seen remarkable advances in medical imaging and the use of personalized models is slowly becoming a reality. Modern imaging techniques can provide an unprecedented amount of anatomical and functional information about the heart and vessels. In this context, three-dimensional, three-directional, cine phase-contrast (PC) magnetic resonance imaging (MRI), commonly referred to as 4D Flow MRI, arises as a powerful tool for creating personalized models. 4D Flow MRI enables the measurement of time-resolved velocity information with volumetric coverage. Besides providing a rich dataset within a single acquisition, the technique permits retrospective analysis of the data at any location within the acquired volume. This thesis focuses on improving subject-specific assessment of cardiovascular function through model-based analysis of 4D Flow MRI data. By using computational models, we aimed to provide mechanistic explanations of the underlying physiological processes, derive novel or improved hemodynamic markers, and estimate quantities that typically require invasive measurements. Paper I presents an evaluation of current markers of stenosis severity using advanced models to simulate flow through a stenosis. Paper II presents a framework to personalize a reduced-order, mechanistic model of the cardiovascular system using exclusively non-invasive measurements, including 4D Flow MRI data. The modelling approach can unravel a number of clinically relevant parameters from the input data, including those representing the contraction and relaxation patterns of the left ventricle, and provide estimations of the pressure-volume loop. In Paper III, this framework is applied to study cardiovascular function at rest and during stress conditions, and the capability of the model to infer load-independent measures of heart function based on the imaging data is demonstrated. Paper IV focuses on evaluating the reliability of the model parameters as a step towards translation of the model to the clinic.
Author: Belén Casas Garcia
Publisher: Linköping University Electronic Press
ISBN: 9176852172
Category :
Languages : en
Pages : 71
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Book Description
Current diagnostic tools for assessing cardiovascular disease mostly focus on measuring a given biomarker at a specific spatial location where an abnormality is suspected. However, as a result of the dynamic and complex nature of the cardiovascular system, the analysis of isolated biomarkers is generally not sufficient to characterize the pathological mechanisms behind a disease. Model-based approaches that integrate the mechanisms through which different components interact, and present possibilities for system-level analyses, give us a better picture of a patient’s overall health status. One of the main goals of cardiovascular modelling is the development of personalized models based on clinical measurements. Recent years have seen remarkable advances in medical imaging and the use of personalized models is slowly becoming a reality. Modern imaging techniques can provide an unprecedented amount of anatomical and functional information about the heart and vessels. In this context, three-dimensional, three-directional, cine phase-contrast (PC) magnetic resonance imaging (MRI), commonly referred to as 4D Flow MRI, arises as a powerful tool for creating personalized models. 4D Flow MRI enables the measurement of time-resolved velocity information with volumetric coverage. Besides providing a rich dataset within a single acquisition, the technique permits retrospective analysis of the data at any location within the acquired volume. This thesis focuses on improving subject-specific assessment of cardiovascular function through model-based analysis of 4D Flow MRI data. By using computational models, we aimed to provide mechanistic explanations of the underlying physiological processes, derive novel or improved hemodynamic markers, and estimate quantities that typically require invasive measurements. Paper I presents an evaluation of current markers of stenosis severity using advanced models to simulate flow through a stenosis. Paper II presents a framework to personalize a reduced-order, mechanistic model of the cardiovascular system using exclusively non-invasive measurements, including 4D Flow MRI data. The modelling approach can unravel a number of clinically relevant parameters from the input data, including those representing the contraction and relaxation patterns of the left ventricle, and provide estimations of the pressure-volume loop. In Paper III, this framework is applied to study cardiovascular function at rest and during stress conditions, and the capability of the model to infer load-independent measures of heart function based on the imaging data is demonstrated. Paper IV focuses on evaluating the reliability of the model parameters as a step towards translation of the model to the clinic.
Author: Roy C.P. Kerckhoffs
Publisher: Springer Science & Business Media
ISBN: 1441966919
Category : Science
Languages : en
Pages : 253
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Book Description
Peter Hunter Computational physiology for the cardiovascular system is entering a new and exciting phase of clinical application. Biophysically based models of the human heart and circulation, based on patient-specific anatomy but also informed by po- lation atlases and incorporating a great deal of mechanistic understanding at the cell, tissue, and organ levels, offer the prospect of evidence-based diagnosis and treatment of cardiovascular disease. The clinical value of patient-specific modeling is well illustrated in application areas where model-based interpretation of clinical images allows a more precise analysis of disease processes than can otherwise be achieved. For example, Chap. 6 in this volume, by Speelman et al. , deals with the very difficult problem of trying to predict whether and when an abdominal aortic aneurysm might burst. This requires automated segmentation of the vascular geometry from magnetic re- nance images and finite element analysis of wall stress using large deformation elasticity theory applied to the geometric model created from the segmentation. The time-varying normal and shear stress acting on the arterial wall is estimated from the arterial pressure and flow distributions. Thrombus formation is identified as a potentially important contributor to changed material properties of the arterial wall. Understanding how the wall adapts and remodels its material properties in the face of changes in both the stress loading and blood constituents associated with infl- matory processes (IL6, CRP, MMPs, etc.
Author:
Publisher:
ISBN: 9781441966926
Category :
Languages : en
Pages : 264
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Author: Yunxiao Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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This dissertation investigates the biomechanics of the left ventricle across multiple geometric levels toward personalized cardiac modeling with engineered heart muscle. Firstly, a physics-based model for the EDPVR and ESPVR of the LV represented by a thick-wall sphere with interpretable parameters is proposed, and the PV relationships of the LV are investigated. Then, the influence of fiber architecture on LV pump function is investigated using an idealized LV model of a truncated ellipsoid. Finally, a workflow for personalized cardiac modeling is developed, which benefits public health by...
Author: Sonali Vyas
Publisher: CRC Press
ISBN: 1003825648
Category : Technology & Engineering
Languages : en
Pages : 301
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Book Description
Handbook on Augmenting Telehealth Services: Using Artificial Intelligence provides knowledge of AI-empowered telehealth systems for efficient healthcare services. The handbook discusses novel innovations in telehealth using AI techniques and also focuses on emerging tools and techniques in smart health systems. The book highlights important topics such as remote diagnosis of patients and presents e-health data management showcasing smart methods that can be used to improvise healthcare support and services. The handbook also shines a light on future trends in AI-enabled telehealth systems. Features Provides knowledge of AI-empowered telehealth systems for efficient healthcare services Discusses novel innovations in telehealth using AI techniques Covers emerging tools and techniques in smart health systems Highlights remote diagnosis of patients Focuses on e-health data management and showcases smart methods used to improvise healthcare support and services Shines a light on future trends in AI-enabled telehealth systems Every individual (patients, doctors, healthcare staff, etc.) is currently getting adapted to this new evolution of healthcare. This handbook is a must-read for students, researchers, academicians, and industry professionals working in the field of artificial intelligence and its uses in the healthcare sector.
Author: Roshan Reddy Upendra
Publisher:
ISBN:
Category : Deep learning (Machine learning)
Languages : en
Pages : 0
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Book Description
"Image-based patient-specific anatomical models of the heart have the potential to be used in a variety of clinical scenarios such as diagnosis and prognosis of various cardiovascular diseases (CVDs), including cardiac resynchronization therapy (CRT), ablation therapy, risk stratification, and minimally invasive cardiac interventions. Cardiac magnetic resonance imaging (MRI) provides images with high-resolution and superior soft tissue contrast, rendering it as the gold standard modality for imaging cardiac anatomy. To obtain meaningful information from such image-based personalized anatomical models of the heart, it is crucial to combine the geometric models of the cardiac chambers extracted from cine cardiac MRI and the scar anatomy from the late gadolinium enhanced (LGE) MRI. There are several challenges to be tackled to generate patient-specific anatomical models of the heart from the cardiac MRI data. Firstly, accurate and robust automated segmentation of the cardiac chambers from the cine cardiac MRI data is essential to estimate cardiac function indices. Secondly, it is important to estimate cardiac motion from 4D cine MRI data to assess the kinematic and contractile properties of the myocardium. Thirdly, accurate registration of the LGE MRI images with their corresponding cine MRI images is crucial to assess myocardial viability. In addition to the above-mentioned segmentation and registration tasks, it is also crucial to computationally super-resolve the anisotropic (high in-plane and low through-plane resolution) cardiac MRI images, while maintaining the structural integrity of the tissues. With the advent of deep learning, medical image segmentation and registration have immensely benefited. In this work, we present a deep learning-based framework to generate personalized cardiac anatomical models using cardiac MRI data. Firstly, we segment the cardiac chambers from an open-source cine cardiac MRI data using an adversarial deep learning framework. We evaluate the viability of the proposed adversarial framework by assessing its effect on the clinical cardiac parameters. Secondly, we propose a convolutional neural network (CNN) based 4D deformable registration algorithm for cardiac motion estimation from an open-source 4D cine cardiac MRI dataset. We extend this proposed CNN-based 4D deformable registration algorithm to develop dynamic patient-specific geometric models of the left ventricle (LV) myocardium and right ventricle (RV) endocardium. Thirdly, we present a deep learning framework for registration of cine and LGE MRI images, and assess the registration performance of the proposed method on an open source dataset. Finally, we present a 3D CNN-based framework with structure preserving gradient guidance to generate super-resolution cardiac MRI images, and assess this proposed super-resolution algorithm on an open-source LGE MRI dataset. Furthermore, we investigate the effect of the proposed super-resolution algorithm on downstream segmentation task."--Abstract.
Author: Selene Pirola
Publisher: Frontiers Media SA
ISBN: 2832529577
Category : Science
Languages : en
Pages : 202
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Book Description
Author: Mariana Bustamante
Publisher: Linköping University Electronic Press
ISBN: 9176853462
Category :
Languages : sv
Pages : 77
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Book Description
Medical image analysis focuses on the extraction of meaningful information from medical images in order to facilitate clinical assessment, diagnostics and treatment. Image processing techniques have gradually become an essential part of the modern health care system, a consequence of the continuous technological improvements and the availability of a variety of medical imaging techniques. Magnetic Resonance Imaging (MRI) is an imaging technique that stands out as non-invasive, highly versatile, and capable of generating high quality images without the use of ionizing radiation. MRI is frequently performed in the clinical setting to assess the morphology and function of the heart and vessels. When focusing on the cardiovascular system, blood flow visualization and quantification is essential in order to fully understand and identify related pathologies. Among the variety of MR techniques available for cardiac imaging, 4D Flow MRI allows for full three-dimensional spatial coverage over time, also including three-directional velocity information. It is a very powerful technique that can be used for retrospective analysis of blood flow dynamics at any location in the acquired volume. In the clinical routine, however, flow analysis is typically done using two-dimensional imaging methods. This can be explained by their shorter acquisition times, higher in-plane spatial resolution and signal-to-noise ratio, and their relatively simpler post-processing requirements when compared to 4D Flow MRI. The extraction of useful knowledge from 4D Flow MR data is especially challenging due to the large amount of information included in these images, and typically requires substantial user interaction. This thesis aims to develop and evaluate techniques that facilitate the post-processing of thoracic 4D Flow MRI by automating the steps necessary to obtain hemodynamic parameters of interest from the data. The proposed methods require little to no user interaction, are fairly quick, make effective use of the information available in the four-dimensional images, and can easily be applied to sizable groups of data.The addition of the proposed techniques to the current pipeline of 4D Flow MRI analysis simplifies and expedites the assessment of these images, thus bringing them closer to the clinical routine. Medicinsk bildanalys fokuserar på extrahering av meningsfull information från medicinska bilder för att underlätta klinisk bedömning, diagnostik, och behandling. Bildbehandlingsteknik har gradvis blivit en viktig del av det moderna sjukvårdsystemet, en följd av de kontinuerliga tekniska förbättringarna och tillgången till en mängd olika medicinska bildtekniker. Magnetic resonanstomografi (MRT) är en bildteknik som är ickeinvasiv, flexibel och kan generera bilder av hög kvalitet utan joniserande strålning. MRT utförs ofta i klinisk miljö för att bedöma anatomi och funktion av hjärtat och blodkärlen. När man fokuserar på hjärt-kärlsystemet är bedömning av blodflödet viktigt för att kunna förstå och identifiera sjukdomar fullt ut. Bland de olika MRT-teknikerna som är tillgängliga för avbildning av hjärtat möjliggör 4D flödes-MRT komplett täckning av hjärtat i tre dimensioner över tid, och med hastighetsinformation i tre riktningar. 4D flödes-MRT är en mycket effektiv metod som kan användas för retrospektiv analys av blodflödesdynamik på vilken position som helst i den avbildade volymen. Till vardags görs dock blodflödesanalysen vanligtvis på bilder tagna med tvådimensionella avbildningsmetoder. Detta kan förklaras av deras kortare insamlingstider, högre spatiella upplösning, bättre signal-brusförhållandet, och att de är relativt enklare att efterbehandla jämfört med 4D flödes-MRT. Utvinningen av användbar information från 4D flödes-MRT-data är väldigt utmanande på grund av den stora mängden information som dessa bilder innehåller och kräver vanligtvis väsentlig användarinteraktion. Denna avhandling syftar till att utveckla och utvärdera metoder som underlättar efterbehandlingen av 4D flödes-MRT genom att automatisera de steg som är nödvändiga för att härleda hemodynamiska parametrarna av intresse från dessa data. De föreslagna metoderna kräver liten eller ingen användarinteraktion, är relativt snabba, använder all information som finns i de fyrdimensionella bilderna, och kan enkelt appliceras på stora datamängder. Tillägget av de i avhandlingen beskrivna metoderna till den nuvarande analysen av 4D flödes-MRT medger en avsevärd förenkling och uppsnabbad utvärdering, vilket gör att den avancerade 4D flödes MRT-tekniken kommer närmare att kunna användas i kliniskt rutinarbete.
Author: David Richard Rutkowski
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Fluid dynamics analysis can provide valuable information for diagnostics and treatment planning of cardiovascular disease. Magnetic resonance imaging (MRI) and computational fluid dynamics (CFD) are both methods that offer a number of advantages when used for fluid dynamic analysis; however, they also have their own set of unique limitations. MRI of the cardiovascular system (CMR) can be used to visualize and quantify parameters such as cardiac volume, cardiac function, great vessel morphology, and many more without harm to the patient. Furthermore, methods known as phase-contrast (PC) MRI offer the ability to visualize blood flow for real-time or retrospective analysis. However, MRI has some limitations in quantitative and predictive cardiovascular analysis when used as a stand-alone method due to resolution limits and errors that result from manipulation of magnetic field, and because of the inherent difficulty of imaging a patient multiple times throughout a disease progression. Fortunately, computational methods can be used to address these limitations. CFD is a method that utilizes the governing equations of fluid flow to compute a flow field, given the appropriate model and conditions. CFD provides high resolution data, and relies on boundary conditions that can be manipulated to match physiological or surgical variations of interest. However, standalone CFD can also be limited due to its high dependence on patient-specific boundary conditions, and its need for appropriate validation with physical blood flow. The work in this thesis was aimed at utilizing the best of both MRI and CFD for cardiovascular fluid dynamic analysis by leveraging the advantages of one method to fill the inherent gaps of the other. This was accomplished through three specific aims. The first was to characterize patient-specific blood flow and anatomy with four-dimensional (4D) flow MRI. The work in Aim 1 entailed using 4D flow MRI to analyze cardiac and vascular blood flow dynamics in congenital heart disease patients with single ventricle defect that have undergone a Fontan palliation surgery - a patient population with very complex blood flow abnormalities. Additionally, sex differences in cardiac flow dynamics of healthy volunteers were analyzed with a prospective study. The second aim was to simulate cardiovascular blood flow with image-based computational simulation. In this aim, MRI-based computational fluid dynamics simulations were performed to analyze hepatic flow dynamics after surgical intervention, as well as portal vein flow patterns in health and disease. The goal of the third aim was to couple imaging and computational methods to improve patient-specific flow results. In this aim, 4D flow MRI and CFD were use synergistically, along with neural network training, to provide high resolution, physics-based, physiological flow fields in patient-specific vascular geometries.
Author: Julius Guccione
Publisher: Frontiers Media SA
ISBN: 2889633233
Category :
Languages : en
Pages : 289
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Book Description
