1,762 research outputs found

    Patient-specific RF safety assessment in MRI: Progress in creating surface-based human head and shoulder models

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    The interaction of electromagnetic (EM) fields with the human body during magnetic resonance imaging (MRI) is complex and subject specific. MRI radiofrequency (RF) coil performance and safety assessment typically includes numerical EM simulations with a set of human body models. The dimensions of mesh elements used for discretization of the EM simulation domain must be adequate for correct representation of the MRI coil elements, different types of human tissue, and wires and electrodes of additional devices. Examples of such devices include those used during electroencephalography, transcranial magnetic stimulation, and transcranial direct current stimulation, which record complementary information or manipulate brain states during MRI measurement. The electrical contact within and between tissues, as well as between an electrode and the skin, must also be preserved. These requirements can be fulfilled with anatomically correct surface-based human models and EM solvers based on unstructured meshes. Here, we report (i) our workflow used to generate the surface meshes of a head and torso model from the segmented AustinMan dataset, (ii) head and torso model mesh optimization for three-dimensional EM simulation in ANSYS HFSS, and (iii) several case studies of MRI RF coil performance and safety assessment

    A brief review of surface meshing in medical images for biomedical computing and visualization

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    A visual representation of the interior of a body is important for clinical analysis and medical intervention. The technique, process and art of creating this visual representation are called medical imaging. The images produced from medical imaging need to be analyses by using Finite Element Method (FEM) especially for intraoperative registration and biomechanical modeling of the tissues. This medical model ranges from the smallest vascular to bones and the complex brain. In order to use FEM, the images need to go through surface meshing generator. Although numerous mesh generation methods have been described to date, there is a few which can deal with medical data input. In this paper, a briefing review of surface meshing that can deal in medical images is presented especially in biomedical computing and visualization. Some automatic mesh generators software used in medical imaging is also discussed such as ScanIP, MIMICS, TETGEN, NetGen, BioMesh3D,CUBITMesh and Gmsh

    Wall Orientation and Shear Stress in the Lattice Boltzmann Model

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    The wall shear stress is a quantity of profound importance for clinical diagnosis of artery diseases. The lattice Boltzmann is an easily parallelizable numerical method of solving the flow problems, but it suffers from errors of the velocity field near the boundaries which leads to errors in the wall shear stress and normal vectors computed from the velocity. In this work we present a simple formula to calculate the wall shear stress in the lattice Boltzmann model and propose to compute wall normals, which are necessary to compute the wall shear stress, by taking the weighted mean over boundary facets lying in a vicinity of a wall element. We carry out several tests and observe an increase of accuracy of computed normal vectors over other methods in two and three dimensions. Using the scheme we compute the wall shear stress in an inclined and bent channel fluid flow and show a minor influence of the normal on the numerical error, implying that that the main error arises due to a corrupted velocity field near the staircase boundary. Finally, we calculate the wall shear stress in the human abdominal aorta in steady conditions using our method and compare the results with a standard finite volume solver and experimental data available in the literature. Applications of our ideas in a simplified protocol for data preprocessing in medical applications are discussed.Comment: 9 pages, 11 figure

    Modeling and Simulation in Engineering

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    This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

    Computational model of the fetal heart with Coarctation of the Aorta

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    Treballs Finals de Grau d'Enginyeria BiomĂšdica. Facultat de Medicina i CiĂšncies de la Salut. Universitat de Barcelona. Curs: 2021-2022. Directors: Patricia Garcia Cañadilla & Bart Bijnens. Tutora: FĂĄtima Crispi.It is thought that altered intrauterine hemodynamics may lead to congenital heart defects, such as aortic arch abnormalities. Coarctation of the aorta (CoA) is one of the most difficult cardiac defects to diagnose before birth, because of the patency of the ductus arteriosus (DA). It consists of a narrowing in the aortic isthmus (AoI) causing a decrease of blood flow. Prenatal diagnosis is important to reduce mortality and morbidity. Nonetheless, prenatal diagnosis has a high rate of false-positive and false-negatives and local hemodynamics in the CoA is not fully understood. The aim of this project was to improve our understanding of the underlying cause of CoA using computational fluid dynamics (CFD) tools. We have implemented a computational model with an idealized geometry of the fetal aorta to investigate the relationship between flow unbalance and wall shear stress (WSS) at the isthmus-ductus. An imbalanced flow was imposed in the ascending aorta (AscAo) and ductus to study if a progressive aortic flow reduction suggests the “flowdependency” of the fetal aortic arch development. As a result, when aortic flow diminished from 50% to 10% progressively, velocity and WSS decreased in the aortic arch and increased in the distal arch. A redistribution of flow could be observed in the model and a “zero flow zone” could be noticed between the brachiocephalic artery and left carotid when the flow decreased to from 50% to 10%. Additionally, another “zero flow zone” could be observed in the AoI when the aortic flow decreased from 50% to 30%

    Towards Individualized Transcranial Electric Stimulation Therapy through Computer Simulation

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    Transkranielle Elektrostimulation (tES) beschreibt eine Gruppe von Hirnstimulationstechniken, die einen schwachen elektrischen Strom ĂŒber zwei nicht-invasiv am Kopf angebrachten Elektroden applizieren. Handelt es sich dabei um einen Gleichstrom, spricht man von transkranieller Gleichstromstimulation, auch tDCS abgekĂŒrzt. Die allgemeine Zielstellung aller Hirnstimulationstechniken ist Hirnfunktion durch ein VerstĂ€rken oder DĂ€mpfen von HirnaktivitĂ€t zu beeinflussen. Unter den Stimulationstechniken wird die transkranielle Gleichstromstimulation als ein adjuvantes Werkzeug zur UnterstĂŒtzung der mikroskopischen Reorganisation des Gehirnes in Folge von Lernprozessen und besonders der Rehabilitationstherapie nach einem Schlaganfall untersucht. Aktuelle Herausforderungen dieser Forschung sind eine hohe VariabilitĂ€t im erreichten Stimulationseffekt zwischen den Probanden sowie ein unvollstĂ€ndiges VerstĂ€ndnis des Zusammenspiels der der Stimulation zugrundeliegenden Mechanismen. Als SchlĂŒsselkomponente fĂŒr das VerstĂ€ndnis der Stimulationsmechanismen wird das zwischen den Elektroden im Kopf des Probanden aufgebaute elektrische Feld erachtet. Einem grundlegenden Konzept folgend wird angenommen, dass Hirnareale, die einer grĂ¶ĂŸeren elektrischen FeldstĂ€rke ausgesetzt sind, ebenso einen höheren Stimulationseffekt erfahren. Damit kommt der Positionierung der Elektroden eine entscheidende Rolle fĂŒr die Stimulation zu. Allerdings verteilt sich das elektrische Feld wegen des heterogenen elektrischen LeitfĂ€higkeitsprofil des menschlichen Kopfes nicht uniform im Gehirn der Probanden. Außerdem ist das Verteilungsmuster auf Grund anatomischer Unterschiede zwischen den Probanden verschieden. Die triviale AbschĂ€tzung der Ausbreitung des elektrischen Feldes anhand der bloßen Position der Stimulationselektroden ist daher nicht ausreichend genau fĂŒr eine zielgerichtete Stimulation. Computerbasierte, biophysikalische Simulationen der transkraniellen Elektrostimulation ermöglichen die individuelle Approximation des Verteilungsmusters des elektrischen Feldes in Probanden basierend auf deren medizinischen Bildgebungsdaten. Sie werden daher zunehmend verwendet, um tDCS-Anwendungen zu planen und verifizieren, und stellen ein wesentliches Hilfswerkzeug auf dem Weg zu individualisierter Schlaganfall-Rehabilitationstherapie dar. Softwaresysteme, die den dahinterstehenden individualisierten Verarbeitungsprozess erleichtern und fĂŒr ein breites Feld an Forschern zugĂ€nglich machen, wurden in den vergangenen Jahren fĂŒr den Anwendungsfall in gesunden Erwachsenen entwickelt. Jedoch bleibt die Simulation von Patienten mit krankhaftem Hirngewebe und strukturzerstörenden LĂ€sionen eine nicht-triviale Aufgabe. Daher befasst sich das hier vorgestellte Projekt mit dem Aufbau und der praktischen Anwendung eines Arbeitsablaufes zur Simulation transkranieller Elektrostimulation. Dabei stand die Anforderung im Vordergrund medizinische Bildgebungsdaten insbesondere neurologischer Patienten mit krankhaft verĂ€ndertem Hirngewebe verarbeiten zu können. Der grundlegende Arbeitsablauf zur Simulation wurde zunĂ€chst fĂŒr gesunde Erwachsene entworfen und validiert. Dies umfasste die Zusammenstellung medizinischer Bildverarbeitungsalgorithmen zu einer umfangreichen Verarbeitungskette, um elektrisch relevante Strukturen in den Magnetresonanztomographiebildern des Kopfes und des Oberkörpers der Probanden zu identifizieren und zu extrahieren. Die identifizierten Strukturen mussten in Computermodelle ĂŒberfĂŒhrt werden und das zugrundeliegende, physikalische Problem der elektrischen Volumenleitung in biologischen Geweben mit Hilfe numerischer Simulation gelöst werden. Im Verlauf des normalen Alterns ist das Gehirn strukturellen VerĂ€nderungen unterworfen, unter denen ein Verlust des Hirnvolumens sowie die Ausbildung mikroskopischer VerĂ€nderungen seiner Nervenfaserstruktur die Bedeutendsten sind. In einem zweiten Schritt wurde der Arbeitsablauf daher erweitert, um diese PhĂ€nomene des normalen Alterns zu berĂŒcksichtigen. Die vordergrĂŒndige Herausforderung in diesem Teilprojekt war die biophysikalische Modellierung der verĂ€nderten Hirnmikrostruktur, da die resultierenden VerĂ€nderungen im LeitfĂ€higkeitsprofil des Gehirns bisher noch nicht in der Literatur quantifiziert wurden. Die Erweiterung des Simulationsablauf zeichnete sich vorrangig dadurch aus, dass mit unsicheren elektrischen LeitfĂ€higkeitswerten gearbeitet werden konnte. Damit war es möglich den Einfluss der ungenau bestimmbaren elektrischen LeitfĂ€higkeit der verschiedenen biologischen Strukturen des menschlichen Kopfes auf das elektrische Feld zu ermitteln. In einer Simulationsstudie, in der Bilddaten von 88 Probanden einflossen, wurde die Auswirkung der verĂ€nderten Hirnfaserstruktur auf das elektrische Feld dann systematisch untersucht. Es wurde festgestellt, dass sich diese GewebsverĂ€nderungen hochgradig lokal und im Allgemeinen gering auswirken. Schließlich wurden in einem dritten Schritt Simulationen fĂŒr Schlaganfallpatienten durchgefĂŒhrt. Ihre großen, strukturzerstörenden LĂ€sionen wurden dabei mit einem höheren Detailgrad als in bisherigen Arbeiten modelliert und physikalisch abermals mit unsicheren LeitfĂ€higkeiten gearbeitet, was zu unsicheren elektrischen FeldabschĂ€tzungen fĂŒhrte. Es wurden individuell berechnete elektrische Felddaten mit der Hirnaktivierung von 18 Patienten in Verbindung gesetzt, unter BerĂŒcksichtigung der inhĂ€renten Unsicherheit in der Bestimmung der elektrischen Felder. Das Ziel war zu ergrĂŒnden, ob die Hirnstimulation einen positiven Einfluss auf die HirnaktivitĂ€t der Patienten im Kontext von Rehabilitationstherapie ausĂŒben und so die Neuorganisierung des Gehirns nach einem Schlaganfall unterstĂŒtzen kann. WĂ€hrend ein schwacher Zusammenhang hergestellt werden konnte, sind weitere Untersuchungen nötig, um diese Frage abschließend zu klĂ€ren.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms BibliographyTranscranial electric current stimulation (tES) denotes a group of brain stimulation techniques that apply a weak electric current over two or more non-invasively, head-mounted electrodes. When employing a direct-current, this method is denoted transcranial direct current stimulation (tDCS). The general aim of all tES techniques is the modulation of brain function by an up- or downregulation of brain activity. Among these, transcranial direct current stimulation is investigated as an adjuvant tool to promote processes of the microscopic reorganization of the brain as a consequence of learning and, more specifically, rehabilitation therapy after a stroke. Current challenges of this research are a high variability in the achieved stimulation effects across subjects and an incomplete understanding of the interplay between its underlying mechanisms. A key component to understanding the stimulation mechanism is considered the electric field, which is exerted by the electrodes and distributes in the subjects' heads. A principle concept assumes that brain areas exposed to a higher electric field strength likewise experience a higher stimulation. This attributes the positioning of the electrodes a decisive role for the stimulation. However, the electric field distributes non-uniformly across subjects' brains due to the heterogeneous electrical conductivity profile of the human head. Moreover, the distribution pattern is variable between subjects due to their individual anatomy. A trivial estimation of the distribution of the electric field solely based on the position of the stimulating electrodes is, therefore, not precise enough for a well-targeted stimulation. Computer-based biophysical simulations of transcranial electric stimulation enable the individual approximation of the distribution pattern of the electric field in subjects based on their medical imaging data. They are, thus, increasingly employed for the planning and verification of tDCS applications and constitute an essential tool on the way to individualized stroke rehabilitation therapy. Software pipelines facilitating the underlying individualized processing for a wide range of researchers have been developed for use in healthy adults over the past years, but, to date, the simulation of patients with abnormal brain tissue and structure disrupting lesions remains a non-trivial task. Therefore, the presented project was dedicated to establishing and practically applying a tES simulation workflow. The processing of medical imaging data of neurological patients with abnormal brain tissue was a central requirement in this process. The basic simulation workflow was first designed and validated for the simulation of healthy adults. This comprised compiling medical image processing algorithms into a comprehensive workflow to identify and extract electrically relevant physiological structures of the human head and upper torso from magnetic resonance images. The identified structures had to be converted to computational models. The underlying physical problem of electric volume conduction in biological tissue was solved by means of numeric simulation. Over the course of normal aging, the brain is subjected to structural alterations, among which a loss of brain volume and the development of microscopic alterations of its fiber structure are the most relevant. In a second step, the workflow was, thus, extended to incorporate these phenomena of normal aging. The main challenge in this subproject was the biophysical modeling of the altered brain microstructure as the resulting alterations to the conductivity profile of the brain were so far not quantified in the literature. Therefore, the augmentation of the workflow most notably included the modeling of uncertain electrical properties. With this, the influence of the uncertain electrical conductivity of the biological structures of the human head on the electric field could be assessed. In a simulation study, including imaging data of 88 subjects, the influence of the altered brain fiber structure on the electric field was then systematically investigated. These tissue alterations were found to exhibit a highly localized and generally low impact. Finally, in a third step, tDCS simulations of stroke patients were conducted. Their large, structure-disrupting lesions were modeled in a more detailed manner than in previous stroke simulation studies, and they were physically, again, modeled by uncertain electrical conductivity resulting in uncertain electric field estimates. Individually simulated electric fields were related to the brain activation of 18 patients, considering the inherently uncertain electric field estimations. The goal was to clarify whether the stimulation exerts a positive influence on brain function in the context of rehabilitation therapy supporting brain reorganization following a stroke. While a weak correlation could be established, further investigation will be necessary to answer that research question.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms Bibliograph
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