Image Based Computational Approaches For Personalized Cardiovascular Medicine Improving Clinical Applicability And Reliability Through Medical Imaging And Experimental Data
Image Based Computational Approaches For Personalized Cardiovascular Medicine Improving Clinical Applicability And Reliability Through Medical Imaging And Experimental Data Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Image Based Computational Approaches For Personalized Cardiovascular Medicine Improving Clinical Applicability And Reliability Through Medical Imaging And Experimental Data book. This book definitely worth reading, it is an incredibly well-written.
Image-based Computational Approaches for Personalized Cardiovascular Medicine: Improving Clinical Applicability and Reliability through Medical Imaging and Experimental Data by Selene Pirola,Amirhossein Arzani,Claudio Chiastra,Francesco Sturla Pdf
Towards Personalized Models of the Cardiovascular System Using 4D Flow MRI by Belén Casas Garcia Pdf
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.
Machine Learning in Cardiovascular Medicine by Subhi J. Al'Aref,Gurpreet Singh,Lohendran Baskaran,Dimitri Metaxas Pdf
Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. It is suitable for both clinicians and engineers; providing a template for clinicians to understand areas of application of machine learning within cardiovascular research; and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine. Provides an overview of machine learning, both for a clinical and engineering audience Summarize recent advances in both cardiovascular medicine and artificial intelligence Discusses the advantages of using machine learning for outcomes research and image processing Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach
Patient-Specific Modeling of the Cardiovascular System by Roy C.P. Kerckhoffs Pdf
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.
Artificial Intelligence for Computational Modeling of the Heart by Tommaso Mansi,Tiziano Passerini,Dorin Comaniciu Pdf
Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient’s heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications. Presents recent advances in computational modeling of heart function and artificial intelligence technologies for subject-specific applications Discusses AI-based technologies for robust anatomical modeling from medical images, data-driven reduction of multi-scale cardiac models, and estimations of physiological parameters from clinical data Illustrates the technology through concrete clinical applications and discusses potential impacts and next steps needed for clinical translation
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges by Oscar Camara,Tommaso Mansi,Mihaela Pop,Kawal Rhode,Maxime Sermesant,Alistair Young Pdf
This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, held in conjunction with MICCAI 2013, in Nagoya, Japan, in September 2013. The 31 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on cardiac image processing; atlas construction; statistical modelling of cardiac function across different patient populations; cardiac mapping; cardiac computational physiology; model customization; atlas based functional analysis; ontological schemata for data and results; integrated functional and structural analyses; as well as the pre-clinical and clinical applicability of these methods.
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges by Tommaso Mansi,Kristin McLeod,Mihaela Pop,Kawal Rhode,Maxime Sermesant,Alistair Young Pdf
This book constitutes the thoroughly refereed post-workshop proceedings of the 7th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges. 7th International Workshop, STACOM 2016, Held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected papers The 24 revised full workshop papers were carefully reviewed and selected from 32 submissions. The papers cover a wide range of topics such as cardiac image processing; atlas construction, statistical modelling of cardiac function across different patient populations; cardiac mapping, cardiac computational physiology; model customization; image-based modelling and image-guided interventional procedures; atlas based functional analysis, ontological schemata for data and results; integrated functional and structural analyses; pre-clinical and clinical applicability of the methods described.
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges by Esther Puyol Anton,Mihaela Pop,Maxime Sermesant,Victor Campello,Alain Lalande,Karim Lekadir,Avan Suinesiaputra,Oscar Camara,Alistair Young Pdf
This book constitutes the proceedings of the 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020, as well as two challenges: M&Ms - The Multi-Centre, Multi-Vendor, Multi-Disease Segmentation Challenge, and EMIDEC - Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI Challenge. The 43 full papers included in this volume were carefully reviewed and selected from 70 submissions. They deal with cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, artificial intelligence, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods.
Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge by Esther Puyol Antón,Mihaela Pop,Carlos Martín-Isla,Maxime Sermesant,Avan Suinesiaputra,Oscar Camara,Karim Lekadir,Alistair Young Pdf
This book constitutes the proceedings of the 12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021, as well as the M&Ms-2 Challenge: Multi-Disease, Multi-View and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. The 25 regular workshop papers included in this volume were carefully reviewed and selected after being revised. They deal with cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, artificial intelligence, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods. In addition, 15 papers from the M&MS-2 challenge are included in this volume. The Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge (M&Ms-2) is focusing on the development of generalizable deep learning models for the Right Ventricle that can maintain good segmentation accuracy on different centers, pathologies and cardiac MRI views. There was a total of 48 submissions to the workshop.
Advanced Algorithmic Approaches to Medical Image Segmentation by S. Kamaledin Setarehdan,Sameer Singh Pdf
Medical imaging is an important topic and plays a key role in robust diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound. This book focuses primarily on model-based segmentation techniques, which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors working in industry and academia, and presents new material.
Systems Biology and Data-Driven Machine Learning-Based Models in Personalized Cardiovascular Medicine by Miguel Hueso,Joan Carles Escolà-Gil,Noemi Rotllan Vila,Alfredo Vellido Pdf
Current and Future Role of Artificial Intelligence in Cardiac Imaging by Steffen Erhard Petersen,Karim Lekadir,Alistair A. Young,Tim Leiner Pdf
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Cardiovascular Imaging and Image Analysis by Ayman El-Baz,Jasjit S. Suri Pdf
This book covers the state-of-the-art approaches for automated non-invasive systems for early cardiovascular disease diagnosis. It includes several prominent imaging modalities such as MRI, CT, and PET technologies. There is a special emphasis placed on automated imaging analysis techniques, which are important to biomedical imaging analysis of the cardiovascular system. Novel 4D based approach is a unique characteristic of this product. This is a comprehensive multi-contributed reference work that will detail the latest developments in spatial, temporal, and functional cardiac imaging. The main aim of this book is to help advance scientific research within the broad field of early detection of cardiovascular disease. This book focuses on major trends and challenges in this area, and it presents work aimed to identify new techniques and their use in biomedical image analysis. Key Features: Includes state-of-the art 4D cardiac image analysis Explores the aspect of automated segmentation of cardiac CT and MR images utilizing both 3D and 4D techniques Provides a novel procedure for improving full-cardiac strain estimation in 3D image appearance characteristics Includes extensive references at the end of each chapter to enhance further study
Medical Imaging Informatics by Alex A.T. Bui,Ricky K. Taira Pdf
Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of biomedical informatics, medical imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of medical imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies.
