4,538 research outputs found
Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches
BackgroundVoxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values.Materials and methodsFluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit).ResultsThe brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration.ConclusionsFor VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration
Design and optimisation of an Intra-Aortic Shrouded rotor axial pump
Undesirable side effects in patients with a LVAD (Left Ventricular Assist Device) pump fitted include blood damage, thrombosis, blood traumatisation, and End-Organ Disfunctions. These side effects have generally been attributed to the high wall shear stresses and the induced turbulent flow. In this study, we introduce a novel design to address these effects by lowering the rotational speed and providing an optimum flow path design to minimise blood damage. We present an initial scheme for a new Intra-Aortic Shrouded Rotary Axial Pump and develop a sequence of pump geometries, for which the Taguchi Design Optimisation Method has been applied. We apply CFD tools to simulate the pressure rise, pump performance, hydraulic efficiency, wall shear stress, exposure time and mass flow rate. A prototype pump has been tested in a mock cardiovascular circuit using a water-glycerol solution. The optimum design delivered the desired pressure/mass flow rate characteristics at a significantly low rpm (2900 rpm). As a result, the estimated blood damage index is low, matching the design requirements. The theoretical performance was matched by experimental results. [Abstract copyright: Crown Copyright © 2023. Published by Elsevier Ltd. All rights reserved.
Efficient convolution-based pairwise elastic image registration on three multimodal similarity metrics
Producción CientÃficaThis paper proposes a complete convolutional formulation for 2D multimodal pairwise image registration problems based on free-form deformations. We have reformulated in terms of discrete 1D convolutions the evaluation of spatial transformations, the regularization term, and their gradients for three different multimodal registration metrics, namely, normalized cross correlation, mutual information, and normalized mutual information. A sufficient condition on the metric gradient is provided for further extension to other metrics. The proposed approach has been tested, as a proof of concept, on contrast-enhanced first-pass perfusion cardiac magnetic resonance images. Execution times have been compared with the corresponding execution times of the classical tensor product formulation, both on CPU and GPU. The speed-up achieved by using convolutions instead of tensor products depends on the image size and the number of control points considered, the larger those magnitudes, the greater the execution time reduction. Furthermore, the speed-up will be more significant when gradient operations constitute the major bottleneck in the optimization process.Ministerio de EconomÃa, Industria y Competitividad (grants TEC2017-82408-R and PID2020-115339RB-I00)ESAOTE Ltd (grant 18IQBM
Multimodal MRI analysis using deep learning methods
Magnetic resonance imaging (MRI) has been widely used in scientific and clinical research. It is a non-invasive medical imaging technique that reveals anatomical structures and provides useful information for investigators to explore aging and pathological processes. Different MR modalities offer different useful properties. Automatic MRI analysis algorithms have been developed to address problems in many applications such as classification, segmentation, and disease diagnosis. Segmentation and labeling algorithms applied to brain MRIs enable evaluations of the volumetric changes of specific structures in neurodegenerative diseases. Reconstruction of fiber orientations using diffusion MRI is beneficial to obtain better understanding of the underlying structures.
In this thesis, we focused on development of deep learning methods for MRI analysis using different image modalities. Specifically, we applied deep learning techniques on different applications, including segmentation of brain structures and reconstruction of tongue muscle fiber orientations. For segmentation of brain structures, we developed an end-to-end deep learning algorithm for ventricle parcellation of brains with ventriculomegaly using T1-w MR images. The deep network provides robust and accurate segmentation results in subjects with high variability in ventricle shapes and sizes. We developed another deep learning method to automatically parcellate the thalamus into a set of thalamic nuclei using T1-w MRI and features from diffusion MRI. The algorithm incorporates a harmonization step to make the network adapt to input images with different contrasts.
We also studied the strains associated with tongue muscles during speech production using multiple MRI modalities. To enable this study, we first developed a deep network to reconstruct crossing tongue muscle fiber orientations using diffusion MRI. The network was specifically designed for the human tongue and accounted for the orthogonality property of the tongue muscles. Next, we proposed a comprehensive pipeline to analyze the strains associated with tongue muscle fiber orientations during speech using diffusion MRI, and tagged and cine MRI. The proposed pipeline provides a solution to analyze the cooperation between muscle groups during speech production
Development of a microphysiological system with integrated electrodes for cardiac cell culture, stimulation and sensing
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: RodrÃguez Trujillo, RomenThis project focuses on the development and experimentation of a microfluidic device with
integrated electrodes, specifically designed to support the growth and maturation of a 3D cardiac
cell matrix. The goal is to create a functional system that not only enables the cells to thrive and
pump, but also facilitates the propagation of electrical stimuli, mimicking the behavior of natural
cardiovascular tissue.
The motivation behind this research stems from the limited regenerative capacity of adult heart
tissue, particularly when it comes to cardiomyocytes. Traditional healing methods are often
inadequate, necessitating heart transplants as the only definitive treatment option. To address this
challenge, scientists are exploring the potential of biomaterial scaffolds to regenerate
cardiovascular tissue by replacing damaged or necrotic tissue.
The microfluidic device developed in this project holds great promise for researchers in the
pharmaceutical field, offering a valuable tool for drug testing and disease modeling. Despite facing
challenges in incorporating gold electrodes into the device, the team has successfully characterized
it using an EIS machine. The design of the microelectrodes and microchannels, along with the
overall functionality of the microchip, have been accomplished. While the current focus has been
on a 2D layer of cells, the future objectives involve achieving a fully functional 3D matrix to fulfill
the original research goals.
Overall, this project aims to represent a significant step towards the advancement of regenerative
medicine and the potential for innovative solutions in treating cardiovascular diseases
A Geometry Modeling and Optimization Pipeline for the Atrioventricular Heart Valves
This research aims to develop a pipeline for modeling the tricuspid heart valve that can be used as an adaptable tool for furthering research in the field, including treatment options for heart valve disease such as functional tricuspid regurgitation. We first gathered data from micro-computed tomography scans of porcine heart valves to extract the valve shape and identify the annulus. The data were transformed and sent to a CAD modeling software in a streamlined process. We then combined the initial shape of the valve with a set of input parameters to define a leaflet surface and chordae tendineae using non-uniform rational B-splines (NURBS). The resulting model was used to represent the valve's shape, which we provide examples of using multiple patient data sets. We also combined the model with a nonlinear isotropic constitutive model for the leaflets to directly use isogeometric analysis (IGA) to evaluate the closure of the valve. This valve model creation, using only a set of initial data and input parameters, was combined with a genetic algorithm search pattern to demonstrate optimization capabilities of the modeling pipeline. The pipeline was used to minimize an objective function for the coaptation area of the valve model, which affects the quality of the valve's closure and reduces chances of tricuspid regurgitation. The presented modeling pipeline provides the next step in streamlining the process from data acquisition to improving biomechanical understanding of the tricuspid valve, bridging the research gap currently present with the tricuspid valve
Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging
No abstract available
Desarrollo de nuevos dispositivos y algoritmos para la monitorización ambulatoria de personas con epilepsia
La epilepsia es una enfermedad crónica con un enorme impacto sociosanitario. Aunque en la actualidad se dispone de una gran cantidad de fármacos antiepilépticos y de otros tratamientos más selectivos como la cirugÃa o la estimulación cerebral, un porcentaje considerable de pacientes no están controlados y continúan teniendo crisis epilépticas. Estas personas suelen vivir condicionadas por la posibilidad de un ataque epiléptico y sus posibles consecuencias, como accidentes, lesiones o incluso la muerte súbita inexplicable. En este contexto, un dispositivo capaz de monitorizar el estado de salud y avisar de un posible ataque epiléptico contribuirÃa a mejorar la calidad de vida de estas personas.
La presente Tesis Doctoral se centra en el desarrollo de un novedoso sistema de monitorización ambulatoria que permita identificar y predecir los ataques epilépticos. Dicho sistema está compuesto por diferentes sensores capaces de registrar de forma sincronizada diferentes señales biomédicas. Mediante técnicas de aprendizaje automático supervisado, se han desarrollado diferentes modelos predictivos capaces de clasificar el estado de la persona epiléptica en normal, preictal (antes de la crisis) e ictal (crisis)
Simulation of heart growth in embryogenesis
The subject of this report is related to the finite element method and its application in the
simulation of a mouse heart in embryogenesis. The focus is on the malformation in criss-
cross heart which is caused by abnormal rotation of the ventricular mass along the long axis
of the heart during embryonic development. The project studies several growth patterns and
boundary conditions that may yield to the experimentaly observed development for regular and
criss-cross heart embryos.Incomin
Respondent or non-respondent comparison post cardiac resynchronisation therapy implantation in patients with dilated cardiomyopathy
Background: Dilated cardiomyopathy can be treated using cardiac resynchronisation therapy (CRT) effectively. In our study, we compared the clinical and biochemical profile of responders and non-responders to CRT device (CRTD) implantation suffering from DCM.
Methods: A cross-sectional observational study was performed in 47 patients with dilated cardiomyopathy for CRTD implantation for a period of 18 months. The tools used for the study include electrocardiography 12 lead, echocardiography: 2D, M mode, Doppler, strain echo, Holter monitoring, coronary angiography and CRTD implantation. Statistical analysis was performed using Epi Info (TM) 7.2.2.2.
Results: The proportion of responders (68.1%) was significantly higher than non-responder (31.9%). Almost 60% of patients in non-responder group had smoking as a risk factor. Around 60% were suffering from hypertension and 33% from T2DM in non-respondent group. Parameters of dyssynchrony has significantly improved in responder group than in non-responder group. LVEDV, LVESV has shown an increase and EF has decreased considerably in DCM patients. Many patients in non-responder category have shown mitral and tricuspid regurgitation. Strain echocardiography parameters-GLS, GRS and GCS were significantly decreased. Post CRTD echocardiographic parameter has improved considerably and LVESV was reduced in more than 15% of responders.
Conclusions: The CRTD implantation improves patients’ clinical and Echocardiographic data which can help in better patient management, improving quality of life and decreased healthcare cost. By this study we can improve patients’ selection and predict accordingly for CRT responders and non-responders and can take necessary measures for better patient’s management
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