Computational fluid dynamics indicators to improve cardiovascular pathologies

In recent years, the study of computational hemodynamics within anatomically complex vascular regions has generated great interest among clinicians. The progress in computational fluid dynamics, image processing and high-performance computing haveallowed us to identify the candidate vascular regions for the appearance of cardiovascular diseases and to predict how this disease may evolve. Medicine currently uses a paradigm called diagnosis. In this thesis we attempt to introduce into medicine the predictive paradigm that has been used in engineering for many years. The objective of this thesis is therefore to develop predictive models based on diagnostic indicators for cardiovascular pathologies. We try to predict the evolution of aortic abdominal aneurysm, aortic coarctation and coronary artery disease in a personalized way for each patient. To understand how the cardiovascular pathology will evolve and when it will become a health risk, it is necessary to develop new technologies by merging medical imaging and computational science. We propose diagnostic indicators that can improve the diagnosis and predict the evolution of the disease more efficiently than the methods used until now. In particular, a new methodology for computing diagnostic indicators based on computational hemodynamics and medical imaging is proposed. We have worked with data of anonymous patients to create real predictive technology that will allow us to continue advancing in personalized medicine and generate more sustainable health systems. However, our final aim is to achieve an impact at a clinical level. Several groups have tried to create predictive models for cardiovascular pathologies, but they have not yet begun to use them in clinical practice. Our objective is to go further and obtain predictive variables to be used practically in the clinical field. It is to be hoped that in the future extremely precise databases of all of our anatomy and physiology will be available to doctors. These data can be used for predictive models to improve diagnosis or to improve therapies or personalized treatments.En els últims anys, l'estudi de l'hemodinàmica computacional en regions vasculars anatòmicament complexes ha generat un gran interès entre els clínics. El progrés obtingut en la dinàmica de fluids computacional, en el processament d'imatges i en la computació d'alt rendiment ha permès identificar regions vasculars on poden aparèixer malalties cardiovasculars, així com predir-ne l'evolució. Actualment, la medicina utilitza un paradigma anomenat diagnòstic. En aquesta tesi s'intenta introduir en la medicina el paradigma predictiu utilitzat des de fa molts anys en l'enginyeria. Per tant, aquesta tesi té com a objectiu desenvolupar models predictius basats en indicadors de diagnòstic de patologies cardiovasculars. Tractem de predir l'evolució de l'aneurisma d'aorta abdominal, la coartació aòrtica i la malaltia coronària de forma personalitzada per a cada pacient. Per entendre com la patologia cardiovascular evolucionarà i quan suposarà un risc per a la salut, cal desenvolupar noves tecnologies mitjançant la combinació de les imatges mèdiques i la ciència computacional. Proposem uns indicadors que poden millorar el diagnòstic i predir l'evolució de la malaltia de manera més eficient que els mètodes utilitzats fins ara. En particular, es proposa una nova metodologia per al càlcul dels indicadors de diagnòstic basada en l'hemodinàmica computacional i les imatges mèdiques. Hem treballat amb dades de pacients anònims per crear una tecnologia predictiva real que ens permetrà seguir avançant en la medicina personalitzada i generar sistemes de salut més sostenibles. Però el nostre objectiu final és aconseguir un impacte en l¿àmbit clínic. Diversos grups han tractat de crear models predictius per a les patologies cardiovasculars, però encara no han començat a utilitzar-les en la pràctica clínica. El nostre objectiu és anar més enllà i obtenir variables predictives que es puguin utilitzar de forma pràctica en el camp clínic. Es pot preveure que en el futur tots els metges disposaran de bases de dades molt precises de tota la nostra anatomia i fisiologia. Aquestes dades es poden utilitzar en els models predictius per millorar el diagnòstic o per millorar teràpies o tractaments personalitzats.Postprint (published version

Novel Strategies in Ischemic Heart Disease

The first edition of this book will provide a comprehensive overview of ischemic heart disease, including epidemiology, risk factors, pathogenesis, clinical presentation, diagnostic tests, differential diagnosis, treatment, complications and prognosis. Also discussed are current treatment options, protocols and diagnostic procedures, as well as the latest advances in the field. The book will serve as a cutting-edge point of reference for the basic or clinical researcher, and any clinician involved in the diagnosis and management of ischemic heart disease. This book is essentially designed to fill the vital gap existing between these practices, to provide a textbook that is substantial and readable, compact and reasonably comprehensive, and to provide an excellent blend of "basics to bedside and beyond" in the field of ischemic heart disease. The book also covers the future novel treatment strategies, focusing on the basic scientific and clinical aspects of the diagnosis and management of ischemic heart disease

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

Development and assessment of new post-processing methodologies in 3D contrast enhanced MRI

This thesis aims to investigate some of the methods currently used in contrast MR imaging. It specifically focuses on methods that require subtraction of noncontrast enhanced (pre) 3D imaging data sets from contrast-enhanced (post) data, collected within a single imaging session. Current methods assume that there is little or no intra-scan patient motion and thus do not attempt to correct for this. This thesis aims to determine if such motion does exist and if so what methods are best suited to correct it. The thesis begins by describing some of the relevant MR physics and history of contrast enhancement in chapter 1, and expands on this in chapter 2 by focusing on angiographic, and contrast-enhanced techniques. Chapter 2 continues by investigating an MP RAGE subtraction technique for producing venograms, which requires pre and post-contrast data subtraction. Data is collected for 20 patients and the effects of motion correction on the resulting venograms are investigated. Chapter 3 investigates a different type of pre and post-contrast enhanced study where it is used for tumour volume measurement. Examining the effects on tumour volumes measured with and without the realignment correction provides quantitative evidence that realignment is a requirement in this and similar types of study. To enable the significance of segmentation accuracy on realigmnent to be tested a phantom pre and post-contrast data set is developed in chapter 4. Chapter 5 uses this data set to test the effects of differing segmentation accuracies, with respect to the accurately segmented phantom data, on realignment accuracy where the pre and post-contrast data differ by known rotations and translations. This provides information on the effects of contrast enhancement on realignment accuracy, as well as providing information on the required brain segmentation accuracy required to accurately realign these data sets. Chapter 6 expands on this work by testing segmentation accuracy effects on two real patient data sets. The first patient data set differs from the phantom data in terms of its noise characteristics and the second has a space occupying lesion similar to those regularly encountered in the clinical setting. Chapter 7 aims to develop an automatic technique for segmenting, realigning and visualising venographic data using the venography technique described in chapter 2. It uses a histogram and morphological operations to ensure that all of the contrast enhanced data is removed from the data, whilst attempting to segment the brain to an acceptable accuracy. Although this algorithm is specifically designed for venograms visualisation, it would require only a small amount of adjustment enabling it to be applied to the tumour volume measurement technique described in chapter 3. Chapter 8 tests this algorithm using the data collected in chapter 2 and measures its performance in producing satisfactory brain segmentations, which is required for accurate realignment. This would also be required for accurate realignment in tumour volume measurement studies. Chapter 8 also measures the algorithms capabilities in correctly producing visualisation data sets for the purposes of venography. The algorithm has limited success in both brain segmentation and venous visualisation, nevertheless this is encouraging as a first attempt as the algorithm is being applied to real patient data sets reflecting a range of pathological conditions and not only to selected normal data sets. Chapter 8 suggests some modifications that could be applied to the algorithm that might improve its future success. This includes modifying it to become a semi-automated technique. (Abstract shortened by ProQuest.)

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on