SMARTech Community: Interdisciplinary Bioengineering Graduate Program (IBGP)

Long-term patency of a polymer vein valve

Tue, 07 Jul 2009 22:58:59 GMT

Title: Long-term patency of a polymer vein valve

Authors: Midha, Prem Anand

Abstract: Chronic Venous Insufficiency (CVI) is a condition in present in almost 27% of adults in which an insufficient amount of blood is pumped back to the heart due to damaged or poorly apposed one-way valves in the leg veins. During forward flow, vein valves allow blood to return to the heart while posing very little resistance to the flow. During gravity-driven reverse flow, normal valves close and prevent blood from flowing backward through the valve. Incompetent, or damaged, vein valves cannot prevent this reverse flow and lead to a pooling of blood at the feet. CVI is a painful disease presents itself in various ways, including varicose veins, ulcerations of the lower extremities, and severe swelling. Current therapies and treatments include compressive stockings, destruction or removal of affected veins, valve repair, and valve transplants. The implantation of prosthetic vein valves is a future treatment option that does not require an invasive surgery, human donor, or lengthy hospital stay. While no prosthetic vein valves are currently commercially available, this thesis describes the design, verification, and validation of a novel prosthetic vein valve. Verification tests include CFD simulations, functional tests, mechanical tests, and in vitro thromogenicity tests. The validation of the device was done through an animal study in sheep external jugular veins. CFD analysis verified that shear rates within the valve support its lower thrombogenicity as compared to a previous vein valve. Benchtop tests demonstrate superiority in short-term patency over a previous polymer valve. In a sheep study, patency was shown at 6 weeks, surpassing many autograft valves and showing great potential to meet the goal of 3 month patency in sheep.

Stucture and thermomechanical behavior of nitipt shape memory alloy wires

Thu, 09 Apr 2009 22:58:59 GMT

Title: Stucture and thermomechanical behavior of nitipt shape memory alloy wires

Authors: Lin, Brian E.

Abstract: The objective of this work is to understand the structure-property relationships in a pseudoelastic composition of polycrystalline NiTiPt (Ti-42.7 at% Ni-7.5 at% Pt). Structural characterization of the alloy includes grain size determination and texture analysis while the thermo-mechanical properties are explored using tensile testing. Variation in heat treatment is used as a vehicle to modify microstructure. The results are compared to experiments on Ni-rich NiTi alloy wires (Ti-51.0 at% Ni), which are in commercial use in various biomedical applications. With regards to microstructure, both alloys exhibit a <111> fiber texture along the wire drawing axis, however the NiTiPt alloy's grain size is smaller than that of the Ni-rich NiTi wires, while the latter materials contain second phase precipitates. Given the nanometer scale grain size in NiTiPt and the dispersed, nanometer scale precipitate size in NiTi, the overall strength and ductility of the alloys are essentially identical when given appropriate heat treatments. Property differences include a much smaller stress hysteresis and smaller temperature dependence of the transformation stress for NiTiPt alloys compared to NiTi alloys. Potential benefits and implications for use in vascular stent applications are discussed.

Techniques for FPGA neural modeling

Mon, 20 Nov 2006 22:58:59 GMT

Title: Techniques for FPGA neural modeling

Authors: Weinstein, Randall Kenneth

Abstract: Neural simulations and general dynamical system modeling consistently push the limits of available computational horsepower. This is occurring for a number of reasons: 1) models are progressing in complexity as our biological understanding increases, 2) high-level analysis tools including parameter searches and sensitivity analyses are becoming more prevalent, and 3) computational models are increasingly utilized alongside with biological preparations in a dynamic clamp configuration. General-purpose computers, as the primary target for modeling problems, are the simplest platform to implement models due to the rich variety of available tools. However, computers, limited by their generality, perform sub-optimally relative to custom hardware solutions. The goal of this thesis is to develop a new cost-effective and easy-to-use platform delivering orders of magnitude improvement in throughput over personal computers. We suggest that FPGAs, or field programmable gate arrays, provide an outlet for dramatically enhanced performance. FPGAs are high-speed, reconfigurable devices that can implement any digital logic operation using an array of parallel computing elements. Already common in fields such as signal processing, radar, medical imaging, and consumer electronics, FPGAs have yet to gain traction in neural modeling due to their steep learning curve and lack of sufficient tools despite their high-performance capability. The overall objective of this work has been to overcome the shortfalls of FPGAs to enable adoption of FPGAs within the neural modeling community. We embarked on an incremental process to develop an FPGA-based modeling environment. We first developed a prototype multi-compartment motoneuron model using a standard digital-design methodology. FPGAs at this point were shown to exceed software simulations by 10x to 100x. Next, we developed canonical modeling methodologies for manual generation of typical neural model topologies. We then developed a series of tools and techniques for analog interfacing, digital protocol processing, and real-time model tuning. This thesis culminates with the development of Dynamo, a fully-automated model compiler for the direct conversion of a model description into an FPGA implementation.

Bayesian based risk stratification of atrial fibrillation in coronary artery bypass graft patients

Mon, 21 May 2007 22:58:59 GMT

Title: Bayesian based risk stratification of atrial fibrillation in coronary artery bypass graft patients

Authors: Wiggins, Matthew Corbin

Abstract: Roughly thirty percent of coronary artery bypass graft (CABG) patients develop atrial fibrillation (AF) in the five days following surgery, increasing the risk of stroke, prolonging hospital stay three to four days, and increasing the overall cost of the procedure. Current pharmacologic and nonpharmacologic means of AF prevention are suboptimal, and their side effects, expense, and inconvenience limit their widespread application. An accurate method for identifying patients at high risk for postoperative AF would allow these methods to be focused on the patients on which its utility would be highest. The main objective of this research was to develop a Bayesian network (BN) which could model/predict/assign risk of the occurrence of atrial fibrillation in CABG patients using retrospective data. A secondary objective was to develop an integrated framework for more advanced methods of feature selection and fusion for medical classification/prediction. We determined that the naïve Bayesian network classifier used with features selected by a genetic algorithm is a better classifier to use, given our cohort. The naïve BN allows for reasonable prediction despite being presented with patients with missing data points as might occur in the hospital. This classifier achieves a sensitivity of 0.63 and a specificity of 0.73 with an AUC of 0.74. Furthermore, this system is based on probabilities that are well understood and easily incorporated into a clinical environment. These probabilities can be altered based on the cardiologists prior knowledge through Bayesian statistics, allowing for online sensitivity analysis by doctors, to perceive the best treatment options. Contributions of this research include: - An accurate, physician-friendly, postoperative AF risk stratification system that performs even under missing data conditions, while outperforming the state of the art system, - A thorough analysis of previously examined and novel pre- and postoperative clinical and ECG features for postoperative AF risk stratification, - A new methodology for genetic algorithm-built traditional Bayesian network classifiers allowing dynamic structure through novel chromosome, operator, and fitness definitions, and - An integrated methodology for inclusion of doctor s expert knowledge into a probabilistic diagnosis support system.