Hemodynamics of Native and Bioprosthetic Aortic Valves: Insights from a Reduced Degree-of-Freedom Model

Heart disease is the leading cause of deaths in the US with aortic valve (AV) diseases being major contributors. Valve replacement is the primary therapeutic indication for AV diseases and transcatheter aortic valve replacement (TAVR) provides a safe and minimally invasive option. However, post-TAVR patient outcomes show considerable variability with deployment parameters. TAVR valves are also susceptible to failure mechanisms like leaflet thrombosis which increase the risk for serious thromboembolic events. Early detection and intervention can avert such outcomes, but symptoms often manifest at advanced stages of valve failure. Continuous monitoring can facilitate early detection, but regulatory and technological challenges may hinder developing such technology through experimental or clinical means. Computer simulations enable unprecedented predictive capabilities which can help gain insights into the pathophysiology of valvular diseases, conduct in silico trials to design novel monitoring technologies and even guide surgeries for optimal valve deployment. However, accurate, yet efficient numerical models are required. This study describes the implementation of a versatile, efficient AV dynamics model in a previously developed fluid-structure interaction solver, and its application to each of these tasks. The model accelerates simulations by simplifying the constitutive parameter space and equations governing leaflet motion without compromising accuracy. It can simulate native and prosthetic valve dynamics exhibiting physiological and pathological function in idealized and personalized aorta anatomies. This computational framework is used to generate canonical and patient-specific simulation datasets describing hemodynamic differences secondary to healthy and pathological AVs. These differences help identify biomarkers which reliably predict the risk of valvular and vascular diseases. Changes in these biomarkers are used to assess whether TAVR can deter aortic disease progression. Next, statistical differences in such biomarkers recorded by virtual wearable or embedded sensor systems, between normal and abnormal AV function, are analyzed using data-driven methods to infer valve health. This lays the groundwork for inexpensive, at-home diagnostic technologies, based on digital auscultation and in situ embedded-sensor platforms. Finally, a simulation describing the deployment of a commercially available TAVR valve in a patient-specific aorta anatomy and the associated hemodynamics is presented. Such simulations empower clinicians to optimize TAVR deployment and, consequently, patient outcomes

Brain Tumour Classification Using Deep Features from Structural MRI

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Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

The mitral valve computational anatomy and geometry analysis

We present a novel methodology to characterize and quantify the Mitral Valve (MV) geometry and physical attributes in a multi-resolution framework. A multi-scale decomposition was implemented to model the MV geometry by using superquadric shape primitives and spectral reconstruction of the finer-scale geometric details. Superquadrics provide a basis to normalize the size and approximate a basic model of the MV geometry. The point-wise difference between the original geometry and the superquadric model denotes the finer-scale geometric details, which can be modeled as a scalar attribute for the MV model development. The additive decomposition of the basic MV geometry from geometric details (attributes) allows recovering the actual geometry by superposition of the superquadric approximation and the finer-details model. We implemented a lasso optimization algorithm to perform spectral analysis and develop the Fourier reconstruction of the geometric details. The spectral modeling enabled us to resample the geometric details or use spectral filters in order to adjust the spatial resolution in the model reconstruction. It also provides the basis to control the level of detail in the final model reconstruction by applying low-pass filters in the frequency domain. The higher-order attributes such as internal fiber architecture can be integrated with the geometric models using the same framework. We applied our pipeline to create models of three ovine MVs based on computed-tomography 3D images with micrometer resolution. We were able to quantify the MV leaflet geometry, reconstruct models with custom level of geometric details, and develop medial representation of the MV leaflet structure. The results show that our methodology for geometry analysis provides a basis for assessing patient-specific geometries and facilitates developing population-averaged models. Ultimately, this approach allows building personalized image-based computational models for medical device design and surgical treatment simulations.Mechanical Engineerin