Traumatic brain injury (TBI) is a great burden for the society worldwide and the statisticsindicates a relative constant total annual rate of TBI. It seems that the present preventativestrategies are not sufficient. To be able to develop head safety measures against accidents ine.g. sports or automobile environment, one needs to understand the mechanism behindtraumatic brain injuries. Through the years, different test subjects have been used, such ascadavers, animals and crash dummies, but there are ethical issues in animal and human testingusing accelerations at injury-level and crash dummies are not completely human-like. In aFinite Element (FE) head model, the complex shape of the intracranial components can bemodeled and mechanical entities, such as pressure, stresses and strains, can be quantified atany theoretical point. It is suggested that the size of the head, the skull-brain boundarycondition, the heterogeneity, and the tethering and suspension system can alter the mechanicalresponse of the brain. It can be seen that the shape of the skull, the composition of gray andwhite matter, the distribution of sulci, the volume of cerebrospinal fluid and geometry of othersoft tissues varies greatly between individuals. All this, suggests the development of patientspecific FE head models.A method to generate patient specific FE head model was contrived based on the geometryfrom Magnetic Resonance Imaging (MRI) scans. The geometry was extracted usingexpectation maximization classification and the mesh of the FE head model was constructedby directly converting the pixel into hexahedral elements. The generated FE model had goodelement quality, the geometrical details were more than 90 % accurate and it correlated wellwith experimental data of relative brain-skull motion. The method was thought to beautomatic but some hypothetically important anatomical structures were not possible to beextracted from medical images. This leads to investigations on the biomechanical influence ofthe cerebral vasculature, the falx and tentorium complex. It was found that biomechanicalinfluence of the cerebral vasculature was minimal, due to the convoluting geometry and thenon-linear elastic material properties of the blood vessels. It suggests that futurebiomechanical FE head model does not necessarily have to include these blood vessels. Theinclusion of falx and tentorium in an FE head model has on the other hand a substantialbiomechanical influence by affecting its surrounding tissue. Therefore, in the investigation ofthe biomechanical influence of the sulci, the falx and tentorium were manually added to theanatomically detailed 3D FE head model. The biomechanical influence of the sulci haspreviously not been studied in 3D and the results indicated an obvious reduction of the strainin the model with sulci compared to the model without sulci in all simulations, and mostinteresting was the consistent reduction of strain in the corpus callosum. The development ofgyri not only produces a larger area for synapses but also forms the sulci to protect the brainfrom external forces.Based on the results, a patient specific FE head model for traumatic brain injury predictionshould at least include the skull, cerebrospinal fluid, falx, tentorium and pia mater, in additionto the brain. With these anatomically detailed 3D models, the injury biomechanics can bebetter understood. Hopefully, the burden of TBI to the society can be alleviated with betterprotective systems and improved understanding of the patients’ condition and hence, theirmedical treatments / QC 20100811

Femoroacetabular impingement (FAI) is recognized as a pathomechanical process that leads to hip osteoarthritis (OA). It is hypothesized that mechanical stimuli are prominent at higher range of motions in hips with cam FAI (aspherical femoral head-neck deformity). Adverse loading conditions can impose elevated mechanical stimuli levels at the articulating surfaces and underlying subchondral bone, which plays a predominant mechanical role in early OA. The aim of this research was to determine the levels of mechanical stimuli within the hip, examining the effects of severe cam impingement on the onset of OA, using patient-specific biomechanics data, CT data, and finite element analysis (FEA). Patient-specific hip joint reaction forces were applied to two symptomatic patient models and two control-matched models, segmented from patient-specific CT data. The finite element models were simulated to compare the locations and magnitudes of mechanical stimuli during two quasi-static positions from standing to squatting. Maximum-shear stress (MSS) was analyzed to determine the adverse loading conditions within the joint and strain energy density (SED) was determined to examine its effect on the initiation of bone remodelling. The results revealed that peak mechanical stimuli concentrations were found on the antero-superior acetabulum during the squatting position, underlying to the cartilage. The MSS magnitudes were significantly higher and concentrated for the FAI patients (15.145 ± 1.715 MPa) in comparison with the MSS magnitudes for the control subjects (4.445 ± 0.085 MPa). The FAI group demonstrated a slight increase in peak SED values on the acetabulum from standing (1.005 ± 0.076 kPa) to squatting (1.018 ± 0.082 kPa). Insignificant changes in SED values were noticed for the control subjects. Squatting orients the femoral head into the antero-superior acetabulum, increasing the contact area with the cartilage and labral regions, thus resulting in higher peaks behind the cartilage on the acetabulum. The resultant location of the peak MSS and SED concentrations correspond well with the region of initial cartilage degradation and early OA observed during open surgical dislocation. Due to the relatively low elastic modulus of the articular cartilage, loads are transferred and amplified to the subchondral bone. This further suggests that elevated stimuli levels can provoke stiffening of the underlying subchondral plate, through bone remodelling, and consequently accelerating the onset of cartilage degradation. Since mechanical stimuli results are unique to their patient-specific loading parameters and conditions, it would be difficult to determine a patient-specific threshold to provoke bone remodeling at this stage.

Femoroacetabular impingement (FAI) is recognized as a pathomechanical process that leads to hip osteoarthritis (OA). It is hypothesized that mechanical stimuli are prominent at higher range of motions in hips with cam FAI (aspherical femoral head-neck deformity). Adverse loading conditions can impose elevated mechanical stimuli levels at the articulating surfaces and underlying subchondral bone, which plays a predominant mechanical role in early OA. The aim of this research was to determine the levels of mechanical stimuli within the hip, examining the effects of severe cam impingement on the onset of OA, using patient-specific biomechanics data, CT data, and finite element analysis (FEA). Patient-specific hip joint reaction forces were applied to two symptomatic patient models and two control-matched models, segmented from patient-specific CT data. The finite element models were simulated to compare the locations and magnitudes of mechanical stimuli during two quasi-static positions from standing to squatting. Maximum-shear stress (MSS) was analyzed to determine the adverse loading conditions within the joint and strain energy density (SED) was determined to examine its effect on the initiation of bone remodelling. The results revealed that peak mechanical stimuli concentrations were found on the antero-superior acetabulum during the squatting position, underlying to the cartilage. The MSS magnitudes were significantly higher and concentrated for the FAI patients (15.145 ± 1.715 MPa) in comparison with the MSS magnitudes for the control subjects (4.445 ± 0.085 MPa). The FAI group demonstrated a slight increase in peak SED values on the acetabulum from standing (1.005 ± 0.076 kPa) to squatting (1.018 ± 0.082 kPa). Insignificant changes in SED values were noticed for the control subjects. Squatting orients the femoral head into the antero-superior acetabulum, increasing the contact area with the cartilage and labral regions, thus resulting in higher peaks behind the cartilage on the acetabulum. The resultant location of the peak MSS and SED concentrations correspond well with the region of initial cartilage degradation and early OA observed during open surgical dislocation. Due to the relatively low elastic modulus of the articular cartilage, loads are transferred and amplified to the subchondral bone. This further suggests that elevated stimuli levels can provoke stiffening of the underlying subchondral plate, through bone remodelling, and consequently accelerating the onset of cartilage degradation. Since mechanical stimuli results are unique to their patient-specific loading parameters and conditions, it would be difficult to determine a patient-specific threshold to provoke bone remodeling at this stage.

This thesis focuses on special arterial fluid mechanics techniques developed for patient-specific computer modeling of blood flow in cerebral arteries with aneurysm and stent. These techniques are used in conjunction with the core computational technique, which is the space–time version of the variational multiscale (VMS) method and is called “DST/SST-VMST.” The special techniques include using NURBS for the spatial representation of the surface over which the stent mesh is built, mesh generation techniques for both the finite- and zero-thickness representations of the stent, techniques for generating refined layers of mesh near the arterial and stent surfaces, and models for representing double stent. We compute the unsteady flow patterns in the aneurysm and investigate how those patterns are influenced by the presence of single and double stents. We also compare the flow patterns obtained with the finite- and zero-thickness representations of the stent.

Femoroacetabular impingement (FAI) is recognized as a pathomechanical process that leads to hip osteoarthritis (OA). It is hypothesized that mechanical stimuli are prominent at higher range of motions in hips with cam FAI (aspherical femoral head-neck deformity). Adverse loading conditions can impose elevated mechanical stimuli levels at the articulating surfaces and underlying subchondral bone, which plays a predominant mechanical role in early OA. The aim of this research was to determine the levels of mechanical stimuli within the hip, examining the effects of severe cam impingement on the onset of OA, using patient-specific biomechanics data, CT data, and finite element analysis (FEA). Patient-specific hip joint reaction forces were applied to two symptomatic patient models and two control-matched models, segmented from patient-specific CT data. The finite element models were simulated to compare the locations and magnitudes of mechanical stimuli during two quasi-static positions from standing to squatting. Maximum-shear stress (MSS) was analyzed to determine the adverse loading conditions within the joint and strain energy density (SED) was determined to examine its effect on the initiation of bone remodelling. The results revealed that peak mechanical stimuli concentrations were found on the antero-superior acetabulum during the squatting position, underlying to the cartilage. The MSS magnitudes were significantly higher and concentrated for the FAI patients (15.145 ± 1.715 MPa) in comparison with the MSS magnitudes for the control subjects (4.445 ± 0.085 MPa). The FAI group demonstrated a slight increase in peak SED values on the acetabulum from standing (1.005 ± 0.076 kPa) to squatting (1.018 ± 0.082 kPa). Insignificant changes in SED values were noticed for the control subjects. Squatting orients the femoral head into the antero-superior acetabulum, increasing the contact area with the cartilage and labral regions, thus resulting in higher peaks behind the cartilage on the acetabulum. The resultant location of the peak MSS and SED concentrations correspond well with the region of initial cartilage degradation and early OA observed during open surgical dislocation. Due to the relatively low elastic modulus of the articular cartilage, loads are transferred and amplified to the subchondral bone. This further suggests that elevated stimuli levels can provoke stiffening of the underlying subchondral plate, through bone remodelling, and consequently accelerating the onset of cartilage degradation. Since mechanical stimuli results are unique to their patient-specific loading parameters and conditions, it would be difficult to determine a patient-specific threshold to provoke bone remodeling at this stage.

Three-dimensional (3-D) medical imaging creates notable opportunities as input toward engineering analyses, whether for basic understanding of the normal function or patho-physiology of an organ, or for the simulation of virtual surgical procedures. These analyses most often require finite element (FE) models to be constructed from patient-specific 3-D medical images. However, creation of such models can be extremely labor-intensive; in addition, image processing and mesh generation are often operator-dependent, lack robustness and may be of suboptimal quality. Focusing on the human aorta, the goal of the present work is to create a fast and robust methodology for quadrilateral surface and hexahedral volume meshing from 3-D medical images with minimal user input. By making use of the segmentation capabilities of the 3-D gradient vector flow field combined with original ray-tracing and orientation control algorithms, we will demonstrate that it is possible to incrementally grow a structured quadrilateral surface mesh of the inner wall of the aorta. The process does not only require minimal input from the user, it is also robust and very fast compared to existing methods; it effectively combines segmentation and meshing into one single effort. After successfully testing the methodology and measuring the quality of the meshes produced by it from synthetic as well as real medical image datasets, we will make use of the surface mesh of the inner aortic wall to derive hexahedral meshes of the aortic wall thickness and of the fluid domain inside the aorta. We will finally outline a tentative approach to merge several structured meshes to process the main branches of the aorta.

abstract: Adaptive processing and classification of electrocardiogram (ECG) signals are important in eliminating the strenuous process of manually annotating ECG recordings for clinical use. Such algorithms require robust models whose parameters can adequately describe the ECG signals. Although different dynamic statistical models describing ECG signals currently exist, they depend considerably on a priori information and user-specified model parameters. Also, ECG beat morphologies, which vary greatly across patients and disease states, cannot be uniquely characterized by a single model. In this work, sequential Bayesian based methods are used to appropriately model and adaptively select the corresponding model parameters of ECG signals. An adaptive framework based on a sequential Bayesian tracking method is proposed to adaptively select the cardiac parameters that minimize the estimation error, thus precluding the need for pre-processing. Simulations using real ECG data from the online Physionet database demonstrate the improvement in performance of the proposed algorithm in accurately estimating critical heart disease parameters. In addition, two new approaches to ECG modeling are presented using the interacting multiple model and the sequential Markov chain Monte Carlo technique with adaptive model selection. Both these methods can adaptively choose between different models for various ECG beat morphologies without requiring prior ECG information, as demonstrated by using real ECG signals. A supervised Bayesian maximum-likelihood (ML) based classifier uses the estimated model parameters to classify different types of cardiac arrhythmias. However, the non-availability of sufficient amounts of representative training data and the large inter-patient variability pose a challenge to the existing supervised learning algorithms, resulting in a poor classification performance. In addition, recently developed unsupervised learning methods require a priori knowledge on the number of diseases to cluster the ECG data, which often evolves over time. In order to address these issues, an adaptive learning ECG classification method that uses Dirichlet process Gaussian mixture models is proposed. This approach does not place any restriction on the number of disease classes, nor does it require any training data. This algorithm is adapted to be patient-specific by labeling or identifying the generated mixtures using the Bayesian ML method, assuming the availability of labeled training data. / Dissertation/Thesis / Ph.D. Electrical Engineering 2012

Three-dimensional (3-D) medical imaging creates notable opportunities as input toward engineering analyses, whether for basic understanding of the normal function or patho-physiology of an organ, or for the simulation of virtual surgical procedures. These analyses most often require finite element (FE) models to be constructed from patient-specific 3-D medical images. However, creation of such models can be extremely labor-intensive; in addition, image processing and mesh generation are often operator-dependent, lack robustness and may be of suboptimal quality. Focusing on the human aorta, the goal of the present work is to create a fast and robust methodology for quadrilateral surface and hexahedral volume meshing from 3-D medical images with minimal user input. By making use of the segmentation capabilities of the 3-D gradient vector flow field combined with original ray-tracing and orientation control algorithms, we will demonstrate that it is possible to incrementally grow a structured quadrilateral surface mesh of the inner wall of the aorta. The process does not only require minimal input from the user, it is also robust and very fast compared to existing methods; it effectively combines segmentation and meshing into one single effort. After successfully testing the methodology and measuring the quality of the meshes produced by it from synthetic as well as real medical image datasets, we will make use of the surface mesh of the inner aortic wall to derive hexahedral meshes of the aortic wall thickness and of the fluid domain inside the aorta. We will finally outline a tentative approach to merge several structured meshes to process the main branches of the aorta.

No description available.

Minimally invasive spine surgery (MISS) has proven to be advantageous over traditional open surgery as it minimizes the likelihood of tissue damage and infections. During MISS, surgeons create small incisions to allow access to the surgery site, however, opting for smaller incisions decreases the surgeon’s field of vision. To compensate, surgeons rely on preoperative and intraoperative ionizing imaging technologies for guidance. Conventional localization of the spine, registration of digital images to the patient during surgery, depends heavily on the surgeon’s anatomical knowledge and their experience. Preoperative images are typically created using 3D technology while intraoperative images use 2D technology. While the integration of preoperative 3D images and intraoperative 2D images can provide valuable assistance, patient’s preoperative and intraoperative positions do not coincide leading to additional use of ionizing imaging. The objective of this research was to propose a workflow that assists with image registration for MISS. The main component of the workflow was the creation of a script that automatically generates patient-specific digital guides, which will then be manufactured, to align the patient’s intraoperative and preoperative body position. By aligning the patient’s positions, the 3D printed surgical guide serves as a shared feature between the preoperative digital image and the actual patient. This allows for the intraoperative image to be registered to the preoperative image more accurately. Additionally, the guide acts as an attachment site for any additional instrument guides/supports. The surgical guide generating script utilizes the skin contour of patient’s torso region, extracted from medical images, to automatically produce the guide’s horizontal and vertical components. Adjustments are made to the components using CAD software before proceeding to manufacturing, via 3D printing, and assembly of the guide. To validate the workflow, more specifically the script’s ability to automatically generate surgical guides that fit over the patient’s back, a guide was created for a mannequin. The maximum gap between the mannequin and the horizontal components was 0.8 cm and 1.5 cm for the vertical component.