Bridging spatiotemporal scales in biomechanical models for living tissues : from the contracting Esophagus to cardiac growth

Appropriate functioning of our body is determined by the mechanical behavior of our organs. An improved understanding of the biomechanical functioning of the soft tissues making up these organs is therefore crucial for the choice for, and development of, efficient clinical treatment strategies focused on patient-specific pathophysiology. This doctoral dissertation describes the passive and active biomechanical behavior of gastrointestinal and cardiovascular tissue, both in the short and long term, through computer models that bridge the cell, tissue and organ scale. Using histological characterization, mechanical testing and medical imaging techniques, virtual esophagus and heart models are developed that simulate the patient-specific biomechanical organ behavior as accurately as possible. In addition to the diagnostic value of these models, the developed modeling technology also allows us to predict the acute and chronic effect of various treatment techniques, through e.g. drugs, surgery and/or medical equipment. Consequently, this dissertation offers insights that will have an unmistakable impact on the personalized medicine of the future.Het correct functioneren van ons lichaam wordt bepaald door het mechanisch gedrag van onze organen. Een verbeterd inzicht in het biomechanisch functioneren van deze zachte weefsels is daarom van cruciale waarde voor de keuze voor, en ontwikkeling van, efficiënte klinische behandelingsstrategieën gefocust op de patiënt-specifieke pathofysiologie. Deze doctoraatsthesis brengt het passieve en actieve biomechanisch gedrag van gastro-intestinaal en cardiovasculair weefsel, zowel op korte als lange termijn, in kaart via computermodellen die een brug vormen tussen cel-, weefsel- en orgaanniveau. Aan de hand van histologische karakterisering, mechanische testen en medische beeldvormingstechnieken worden virtuele slokdarm- en hartmodellen ontwikkeld die het patiënt-specifieke orgaangedrag zo accuraat mogelijk simuleren. Naast de diagnostische waarde van deze modellen, laat de ontwikkelde modelleringstechnologie ook toe om het effect van verschillende behandelingstechnieken, via medicatie, chirurgie en/of medische apparatuur bijvoorbeeld, acuut en chronisch te voorspellen. Bijgevolg biedt deze doctoraatsthesis inzichten die een onmiskenbare impact zullen hebben op de gepersonaliseerde geneeskunde van de toekomst

Biomechanical Models of Human Upper and Tracheal Airway Functionality

The respiratory tract, in other words, the airway, is the primary airflow path for several physiological activities such as coughing, breathing, and sneezing. Diseases can impact airway functionality through various means including cancer of the head and neck, Neurological disorders such as Parkinson\u27s disease, and sleep disorders and all of which are considered in this study. In this dissertation, numerical modeling techniques were used to simulate three distinct airway diseases: a weak cough leading to aspiration, upper airway patency in obstructive sleep apnea, and tongue cancer in swallow disorders. The work described in this dissertation, therefore, divided into three biomechanical models, of which fluid and particulate dynamics model of cough is the first. Cough is an airway protective mechanism, which results from a coordinated series of respiratory, laryngeal, and pharyngeal muscle activity. Patients with diminished upper airway protection often exhibit cough impairment resulting in aspiration pneumonia. Computational Fluid Dynamics (CFD) technique was used to simulate airflow and penetrant behavior in the airway geometry reconstructed from Computed Tomography (CT) images acquired from participants. The second study describes Obstructive Sleep Apnea (OSA) and the effects of dilator muscular activation on the human retro-lingual airway in OSA. Computations were performed for the inspiration stage of the breathing cycle, utilizing a fluid-structure interaction (FSI) method to couple structural deformation with airflow dynamics. The spatiotemporal deformation of the structures surrounding the airway wall was predicted and found to be in general agreement with observed changes in luminal opening and the distribution of airflow from upright to supine posture. The third study describes the effects of cancer of the tongue base on tongue motion during swallow. A three-dimensional biomechanical model was developed and used to calculate the spatiotemporal deformation of the tongue under a sequence of movements which simulate the oral stage of swallow

Aortic dissection: simulation tools for disease management and understanding

Aortic dissection is a severe cardiovascular pathology in which a tear in the intimal layer of the aortic wall allows blood to flow between the vessel wall layers, forming a 'false lumen'. In type-B aortic dissections, those involving only the descending aorta, the decision to medically manage or surgically intervene is not clear and is highly dependent on the patient. In addition to clinical imaging data, clinicians would benefit greatly from additional physiological data to inform their decision-making process. Computational fluid dynamics methods show promise for providing data on haemodynamic parameters in cardiovascular diseases, which cannot otherwise be predicted or safely measured. The assumptions made in the development of such models have a considerable impact on the accuracy of the results, and thus require careful investigation. Application of appropriate boundary conditions is a challenging but critical component of such models. In the present study, imaging data and invasive pressure measurements from a patient with a type-B aortic dissection were used to assist numerical modelling of the haemodynamics in a dissected aorta. A technique for tuning parameters for coupled Windkessel models was developed and evaluated. Two virtual treatments were modelled and analysed using the developed dynamic boundary conditions. Finally, the influence of wall motion was considered, of which the intimal flap that separates the false lumen from the true lumen, is of particular interest. The present results indicate that dynamic boundary conditions are necessary in order to achieve physiologically meaningful flows and pressures at the boundaries, and hence within the dissected aorta. Additionally, wall motion is of particular importance in the closed regions of the false lumen, wherein rigid wall simulations fail to capture the motion of the fluid due to the elasticity of the vessel wall and intimal flap

Proceedings, MSVSCC 2014

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

Biomechanics and Remodelling for Design and Optimisation in Oral Prosthesis and Therapeutical Procedure

The purpose of dental prostheses is to restore the oral function for edentulous patients. Introducing any dental prosthesis into mouth will alter biomechanical status of the oral environment, consequently inducing bone remodelling. Despite the advantageous benefits brought by dental prostheses, the attendant clinical complications and challenges, such as pain, discomfort, tooth root resorption, and residual ridge reduction, remain to be addressed. This thesis aims to explore several different dental prostheses by understanding the biomechanics associated with the potential tissue responses and adaptation, and thereby applying the new knowledge gained from these studies to dental prosthetic design and optimisation. Within its biomechanics focus, this thesis is presented in three major clinical areas, namely prosthodontics, orthodontics and dental implantology. In prosthodontics, the oral mucosa plays a critical role in distributing occlusal forces a denture to the underlying bony structure, and its response is found in a complex, dynamic and nonlinear manner. It is discovered that interstitial fluid pressure in mocosa is the most important indicator to the potential resorption induced by prosthetic denture insertion, and based on this finding, patient-specific analysis is performed to investigate the effects caused by various types of dentures and prediction of the bone remodelling activities. In orthodontic treatments, a dynamic algorithm is developed to analyse and predict potential bone remodelling around the target tooth during orthodontic treatment, thereby providing a numerical approach for treatment planning. In dental implantology, a graded surface morphology of an implant is designed to improve osseointegration over that of a smooth uniform surface in both the short and long term. The graded surface can be optimised to achieve the best possible balance between the bone-implant contact and the peak Tresca stress for the specific clinical application need

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Método computacional para segmentação não supervisionada de imagens histológicas de linfoma

Histological image analysis represents a major evolutionary step in modern medicine. Associated with this step, computational methods are being widely developed to help specialists during the analysis of these images to determine diagnostics, prognostics and appropriate treatments in accordance with the condition of the patient. However, when it is performed by specialists, this task becomes time-consuming and susceptible to inter- and intra-pathologist variability. To improve this traditional practice for diagnostics of Mantle Cell Lymphoma, Follicular Lymphoma and Chronic Lymphocytic Leukemia, this study proposes a method for the unsupervised segmentation of nuclear components in indicative cells of such neoplasias using histological images stained with Hematoxylin-Eosin. The proposed method was divided into preprocessing, segmentation and post processing. In the preprocessing step, the techniques used in histogram equalization and Gaussian filter were applied to the channels from RGB color model. In the segmentation, a thresholding technique was applied combining the methods of fuzzy 3-partition entropy and genetic algorithm. Finally, for the improvement of the segmentation results, morphological operations and the valley-emphasis technique were used. For evaluating the developed method, histological images of lymphoma with magnification 20x were selected and manually segmented by a specialist. Those reference images (gold standard) allowed the extraction of quantitative measures in order to compare this method with different techniques proposed in the literature. Furthermore, a qualitative evaluation was conducted leading to relevant and improved results over those from compared studies. Its application was also analysed considering the steps of feature extraction and classification of the lesions, obtaining results of accuracy close to 100%FAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas GeraisCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorMestre em Ciência da ComputaçãoA análise de imagens histológicas representa uma das maiores evoluções da medicina moderna. Aliados a essa evolução, métodos computacionais vêm sendo amplamente desenvolvidos para auxiliar especialistas na análise dessas imagens para determinar diagnósticos, prognósticos e tratamentos adequados à condição do paciente. Porém, ao ser realizada por especialistas, essa tarefa torna-se dispendiosa e suscetível a variabilidades inter e intrapatologistas. Para aperfeiçoar tal prática tradicional para diagnósticos de Linfoma de Células do Manto, Linfoma Folicular e Leucemia Linfóide Crônica, este trabalho propõe um método para a segmentação não supervisionada dos componentes nucleares de células indicativas de tais neoplasias utilizando imagens histológicas coradas com Hematoxilina-Eosina. O método proposto foi dividido nas etapas de pré-processamento, segmentação e pós-processamento. Na etapa de pré-processamento, as técnicas de equalização do histograma e filtro gaussiano foram aplicadas sobre os canais componentes do modelo de cores RGB. Na segmentação, foi aplicada uma técnica de limiarização resultante da combinação entre os métodos fuzzy 3-partition entropy e algoritmo genético. Por fim, para aperfeiçoamento dos resultados da segmentação, foram utilizadas operações morfológicas e a técnica valley-emphasis. Para avaliar o método desenvolvido, imagens histológicas de linfoma com magnificação 20x foram selecionadas e segmentadas manualmente por um especialista. Essas imagens de referência (padrão-ouro) permitiram a extração de medidas quantitativas para a comparação entre este método e diferentes técnicas propostas na literatura. Além disso, uma avaliação qualitativa foi realizada levando a resultados relevantes e superiores aos trabalhos comparados. Também foi analisada a sua aplicação sobre as etapas de extração de características e classificação das diferentes lesões consideradas, obtendo resultados de acurácia próximos a 100%