Vessel tractography using an intensity based tensor model

In the last decade, CAD (Coronary Artery Disease) has been the leading cause of death worldwide [1]. Extraction of arteries is a crucial step for accurate visualization, quantification, and tracking of pathologies. However, coronary artery segmentation is one of the most challenging problems in medical image analysis, since arteries are complex tubular structures with bifurcations, and have possible pathologies. Moreover, appearance of blood vessels and their geometry can be perturbed by stents, calcifications and pathologies such as stenosis. Besides, noise, contrast and resolution artifacts can make the problem more challenging. In this thesis, we present a novel tubular structure segmentation method based on an intensity-based tensor that fits to a vessel, which is inspired from diffusion tensor image (DTI) modeling. The anisotropic tensor inside the vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized with a single seed point and it is capable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrate the performance of our algorithm on 3 complex tubular structured synthetic datasets, and on 8 CTA (Computed Tomography Angiography) datasets (from Rotterdam Coronary Artery Algorithm Evaluation Framework) for quantitative validation. Additionally, extracted arteries from 10 CTA volumes are qualitatively evaluated by a cardiologist expert's visual scores

Mathematical models for glioma growh and migration inside the brain

284 p.Los gliomas forman el subtipo más prevalente, agresivo e invasivo de tumores cerebrales primarios,caracterizados por una rápida proliferación celular y una elevada capacidad de infiltración. A pesar de los avances de la investigación clínica, estos tumores suelen ser resistentes al tratamiento; la supervivencia media oscila entre 9 y 12 meses, siendo la recurrencia la principal causa de mortalidad.La migración y la invasión de los gliomas en el cerebro son fenómenos complejos y aún se desconocen varios de los mecanismos subyacentes que guían la progresión de estos tumores.En esta tesis, proponemos varios modelos matemáticos para estudiar diversos aspectos de la progresión del glioma en relación con las escalas microscópicas y macroscópicas que caracterizan este proceso. Considerar el carácter intrínsico multiescala de la evolución del glioma permite definir modelos basados en sistemas dinámicos, ecuaciones cinéticas y EDP macroscópicas con diferentes roles dependiendo de los fenómenos a estudiar. Uno de los objetivos principales de esta tesis es integrar datos biológicos y clínicos con los modelos matemáticos. Los datos experimentales utilizados se han obtenido de imágenes por resonancia magnética, de imágenes con tensor de difusión del cerebro humano y de análisis de inmunofluorescencia in vivo de distribuciones de varias proteínas en Drosophila, un modelo fiable para el estudio de la dinámica del glioblastoma.Analizamos las características de anisotropía del tejido nervioso, utilizando los datos del tensor de difusión, y la influencia de la estructura de las fibras en la dinámica de las células tumorales.Mostramos cómo la red de fibras guía la migración celular a lo largo de rutas preferenciales,reproduciendo los patrones ramificados y heterogéneos típicos de la evolución del glioma; asimismo,demostramos cómo los tratamientos multimodales pueden reducir este comportamiento.Estudiamos la interdependencia entre la acidez del microambiente y la vascularización en el proceso de angiogénesis tumoral. Para ello, construimos un modelo capaz de reproducir la influencia de estos mecanismos en el desarrollo de la heterogeneidad intratumoral y de características típicas de la progresión del glioma relacionadas con la hipoxia (e.g. la necrosis). Este estudio permite formular una clasificación de los tumores basada en el nivel de necrosis, así como la investigación de terapias multimodales que incluyan efectos antiangiogénicos.Investigamos la influencia de las protrusiones celulares desde una perspectiva no local.Analizamos su rol en el fenómeno de la guía por contacto y en la manifestación de efectos colaborativos o competitivos entre dos estímulos que determinan cambios de dirección de la velocidad celular.Utilizando el análisis experimental de las distribuciones de varias proteínas, evaluamos la relación de las protrusiones celulares con las integrinas y las proteasas como principales mecanismos de progresión del glioblastoma. Mostramos cómo las interacciones bioquímicas y biomecánicas de estos agentes dan como resultado el desarrollo de frentes de propagación tumoral, que pueden presentar una evolución dinámica y heterogénea en relación a los cambios ambientales.bcam:basque center for applied mathematics; La Caixa Foundatio

Mathematical models for glioma growth and migration inside the brain

Gliomas are the most prevalent, aggressive, and invasive subtype of primary brain tumors, characterized by rapid cell proliferation and great infiltration capacity. De- spite the advances of clinical research, these tumors are often resistant to treatment, the median survival ranges between 9 and 12 months, and recurrence is the main cause of mortality. Glioma migration and invasion into the brain tissue is a complex phenomenon and little is still known about the underlying mechanisms that lead to tumor progression. In this thesis, we propose several mathematical models studying various aspects of glioma progression in relation to the microscopic and macroscopic scales charac- terizing this process. Exploiting the inherently multiscale nature of glioma evolution allows to define models based on dynamical systems, kinetic equations, and macro- scopic PDEs with different roles depending on the considered phenomena. The in- tegration of biological and clinical data with the mathematical models is one of the key objectives of this thesis. The experimental data at hand are obtained from mag- netic resonance and diffusion tensor images of the human brain and from in-vivo im- munofluorescence analysis of protein distributions in Drosophila, a reliable model for the study of glioblastoma dynamics. We analyze the anisotropic characteristics of the brain tissue, using the diffusion tensor data, and the influence of the fiber structures on tumor cell dynamics. We show how the fiber network directs cell migration along preferential paths, reproducing the branched and heterogeneous patterns typical of glioma evolution, and how multi- modal treatments can reduce this behavior. We study the interdependency of microenvironmental acidity and vasculature in tumor angiogenesis, defining a model capable of reproducing their influence on the emergence of phenotypic heterogeneity and hypoxia-related features (like necrosis) typical of glioma progression. This study enables the testing of a necrosis-based tumor grading and the investigation of multi-modal therapies with anti-angiogenic effects. We investigated the role of cell protrusions from a non-local perspective. We ex- plore their influence on the contact guidance phenomenon and on the emergence of collaborative or competitive effects between two cues driving cell velocity changes. Using the experimental analysis of protein distributions, we evaluate cell protru- sion relationship with integrins and proteases as leading mechanisms of glioblastoma progression. We show how the biochemical and biomechanical interactions of these agents result in the emergence of tumor propagation fronts, which can feature a dy- namical and heterogenous evolution in relation to environmental changes.European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713673. ”la Caixa” Foundation (ID 100010434), with fellowship code LCF/BQ/IN17/11620056

Mathematical models for glioma growh and migration inside the brain

284 p.Los gliomas forman el subtipo más prevalente, agresivo e invasivo de tumores cerebrales primarios,caracterizados por una rápida proliferación celular y una elevada capacidad de infiltración. A pesar de los avances de la investigación clínica, estos tumores suelen ser resistentes al tratamiento; la supervivencia media oscila entre 9 y 12 meses, siendo la recurrencia la principal causa de mortalidad.La migración y la invasión de los gliomas en el cerebro son fenómenos complejos y aún se desconocen varios de los mecanismos subyacentes que guían la progresión de estos tumores.En esta tesis, proponemos varios modelos matemáticos para estudiar diversos aspectos de la progresión del glioma en relación con las escalas microscópicas y macroscópicas que caracterizan este proceso. Considerar el carácter intrínsico multiescala de la evolución del glioma permite definir modelos basados en sistemas dinámicos, ecuaciones cinéticas y EDP macroscópicas con diferentes roles dependiendo de los fenómenos a estudiar. Uno de los objetivos principales de esta tesis es integrar datos biológicos y clínicos con los modelos matemáticos. Los datos experimentales utilizados se han obtenido de imágenes por resonancia magnética, de imágenes con tensor de difusión del cerebro humano y de análisis de inmunofluorescencia in vivo de distribuciones de varias proteínas en Drosophila, un modelo fiable para el estudio de la dinámica del glioblastoma.Analizamos las características de anisotropía del tejido nervioso, utilizando los datos del tensor de difusión, y la influencia de la estructura de las fibras en la dinámica de las células tumorales.Mostramos cómo la red de fibras guía la migración celular a lo largo de rutas preferenciales,reproduciendo los patrones ramificados y heterogéneos típicos de la evolución del glioma; asimismo,demostramos cómo los tratamientos multimodales pueden reducir este comportamiento.Estudiamos la interdependencia entre la acidez del microambiente y la vascularización en el proceso de angiogénesis tumoral. Para ello, construimos un modelo capaz de reproducir la influencia de estos mecanismos en el desarrollo de la heterogeneidad intratumoral y de características típicas de la progresión del glioma relacionadas con la hipoxia (e.g. la necrosis). Este estudio permite formular una clasificación de los tumores basada en el nivel de necrosis, así como la investigación de terapias multimodales que incluyan efectos antiangiogénicos.Investigamos la influencia de las protrusiones celulares desde una perspectiva no local.Analizamos su rol en el fenómeno de la guía por contacto y en la manifestación de efectos colaborativos o competitivos entre dos estímulos que determinan cambios de dirección de la velocidad celular.Utilizando el análisis experimental de las distribuciones de varias proteínas, evaluamos la relación de las protrusiones celulares con las integrinas y las proteasas como principales mecanismos de progresión del glioblastoma. Mostramos cómo las interacciones bioquímicas y biomecánicas de estos agentes dan como resultado el desarrollo de frentes de propagación tumoral, que pueden presentar una evolución dinámica y heterogénea en relación a los cambios ambientales.bcam:basque center for applied mathematics; La Caixa Foundatio

Multi-scale imaging and modelling of bone

The multi-level organization of bone facilitates the exploitation of in-vivo micro-scale information which is currently lacking for clinical applications. The three sub-projects presented in this thesis investigate the human skeletal system at multiple scales using magnetic resonance imaging (MRI) with the aim of providing new techniques for extracting finer scale information in-vivo. At the whole organ level, human knee joint kinematics was studied using a combined MRI strategy. This new strategy enables the in-vivo investigation of tibiofemoral locomotion under body weight-bearing conditions by modelling the knee flexion angle as a function of the femur and tibia cartilage surfaces in contact. The resultant "contact" trajectory may potentially be used to understand the mechanical cause of cartilage degeneration and as a biomarker to detect abnormalities in the lower limb. At the molecular level, in-vivo MR diffusion tensor imaging (DTI) has been performed for the first time in the human tibia epiphysis. By tracking the water molecules inside the red marrow, the organization of trabecular bone network may be understood as the streamlines formed by anisotropic diffusion trajectories. This sub-project aims to understand the organization of trabecular bone networks non-invasively, which is usually performed ex-vivo through biopsies. The feasibility and reproducibility of DTI is studied. Finally, a new MR imaging protocol named multi-directional sub-pixel enhancement (mSPENT) is proposed and developed to quantify the trabecular bone structural arrangement at the meso-scale. By modulating a dephasing gradient to manipulate the underlying spin system inside each voxel, the resulting mSPENT image contrast varies with gradient at different directions based on the magnetization at the corresponding voxel. A tensor-based method is further developed to model this contrast change, leading to a localized quantification of tissue structural orientation beyond the conventional MR imaging resolution

Multiscale mechano-morphology of soft tissues : a computational study with applications to cancer diagnosis and treatment

Cooperation of engineering and biomedical sciences has produced significant advances in healthcare technology. In particular, computational modelling has led to a faster development and improvement of diagnostic and treatment techniques since it allows exploring multiple scenarios without additional complexity and cost associated to the traditional trial-and-error methodologies. The goal of this thesis is to propose computational methodologies to analyse how the changes in the microstructure of soft tissues, caused by different pathological conditions, influence the mechanical properties at higher length scales and, more importantly, to detect such changes for the purpose of quantitative diagnosis and treatment of such pathologies in the scenario of drug delivery. To achieve this objective different techniques based on quasi-static and dynamic probing have been established to perform quantitative tissue diagnosis at the microscopic (tissue) and macroscopic (organ) scales. The effects of pathologies not only affect the mechanical properties of tissue (e.g. elasticity and viscoelasticity), but also the transport properties (e.g. diffusivity) in the case of drug delivery. Such transport properties are further considered for a novel multi-scale, patient-specific framework to predict the efficacy of chemotherapy in soft tissues. It is hoped that this work will pave the road towards non-invasive palpation techniques for early diagnosis and optimised drug delivery strategies to improve the life quality of patientsJames-Watt Scholarship, Heriot-Watt Annual Fund and the Institute of Mechanical, Process and Energy Engineering (IMPEE) Grant

Adaptive processing of thin structures to augment segmentation of dual-channel structural MRI of the human brain

This thesis presents a method for the segmentation of dual-channel structural magnetic resonance imaging (MRI) volumes of the human brain into four tissue classes. The state-of-the-art FSL FAST segmentation software (Zhang et al., 2001) is in widespread clinical use, and so it is considered a benchmark. A significant proportion of FAST’s errors has been shown to be localised to cortical sulci and blood vessels; this issue has driven the developments in this thesis, rather than any particular clinical demand. The original theme lies in preserving and even restoring these thin structures, poorly resolved in typical clinical MRI. Bright plate-shaped sulci and dark tubular vessels are best contrasted from the other tissues using the T2- and PD-weighted data, respectively. A contrasting tube detector algorithm (based on Frangi et al., 1998) was adapted to detect both structures, with smoothing (based on Westin and Knutsson, 2006) of an intermediate tensor representation to ensure smoothness and fuller coverage of the maps. The segmentation strategy required the MRI volumes to be upscaled to an artificial high resolution where a small partial volume label set would be valid and the segmentation process would be simplified. A resolution enhancement process (based on Salvado et al., 2006) was significantly modified to smooth homogeneous regions and sharpen their boundaries in dual-channel data. In addition, it was able to preserve the mapped thin structures’ intensities or restore them to pure tissue values. Finally, the segmentation phase employed a relaxation-based labelling optimisation process (based on Li et al., 1997) to improve accuracy, rather than more efficient greedy methods which are typically used. The thin structure location prior maps and the resolution-enhanced data also helped improve the labelling accuracy, particularly around sulci and vessels. Testing was performed on the aged LBC1936 clinical dataset and on younger brain volumes acquired at the SHEFC Brain Imaging Centre (Western General Hospital, Edinburgh, UK), as well as the BrainWeb phantom. Overall, the proposed methods rivalled and often improved segmentation accuracy compared to FAST, where the ground truth was produced by a radiologist using software designed for this project. The performance in pathological and atrophied brain volumes, and the differences with the original segmentation algorithm on which it was based (van Leemput et al., 2003), were also examined. Among the suggestions for future development include a soft labelling consensus formation framework to mitigate rater bias in the ground truth, and contour-based models of the brain parenchyma to provide additional structural constraints