Biomedical Engineering and Bioengineering Commons^™

Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa

Theses and Dissertations

Bone marrow lesions (BMLs), occurs from fluid build up in the soft tissues inside your bone. This can be seen on magnetic resonance imaging (MRI) scans and is characterized by excess water signals in the bone marrow space. This disease is commonly caused by osteoarthritis (OA), a degenerative join disease where tissues within the joint breakdown over time [1]. These BMLs are an emerging target for OA, as they are commonly related to pain and worsening of the diseased area until surgical intervention is required [2]–[4]. In order to assess the BMLs, MRIs were utilized as input into a regression …

Methodology For Designing Assistive Robots For Activities Of Daily Living Assistance, Javier Dario Sanjuan De Caro

Theses and Dissertations

The growing prevalence of Upper or Lower Extremities Dysfunctions (ULED), often linked to central nervous disorders such as stroke, Spinal Cord Injury (SCI), and Multiple Sclerosis (MS), underscores the urgent need for innovative support solutions. Over 5.35 million Americans currently live with ULED, a situation that places a significant socioeconomic burden on families and society. Despite invaluable support from caregivers and family members, the need for more scalable, practical solutions persists.

Wheelchair-mounted assistive robots emerge as a promising alternative in this context. These devices, offering continuous and reliable assistance, significantly alleviate caregiver fatigue and enhance the independence and quality of …

Image-Based Cancer Diagnosis Using Novel Deep Neural Networks, Hosein Barzekar

Theses and Dissertations

Cancer is the major cause of death in many nations. This serious illness can only be effectivelytreated if it is diagnosed early. In contrast, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by modest modifications in individual cells or tissues, making them difficult to detect visually. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial expense, making the classification of …

Universal Design In Bci: Deep Learning Approaches For Adaptive Speech Brain-Computer Interfaces, Srdjan Lesaja

Theses and Dissertations

In the last two decades, there have been many breakthrough advancements in non-invasive and invasive brain-computer interface (BCI) systems. However, the majority of BCI model designs still follow a paradigm whereby neural signals are preprocessed and task-related features extracted using static, and generally customized, data-independent designs. Such BCI designs commonly optimize narrow task performance over generalizability, adaptability, and robustness, which is not well suited to meeting individual user needs. If one day BCIs are to be capable of decoding our higher-order cognitive commands and conceptual maps, their designs will need to be adaptive architectures that will evolve and grow in …

An Innovative Triboelectric Stent Sensor: Prospective Cardiovascular Health Monitor Device, Ulises Vidaurri Romero

Theses and Dissertations

Previous research that has focused on TENGs have lacked the proper application of this energy generator. Currently, heart disease remains the leading cause of death in the United States. With increasing demand for self-sustainable medical devices, this nitinol health monitor sensor device (NHMS) integrates the TENG applications with medical applications. The NHMS features memory shape nitinol electrodes that preserves the device structure while using PDMS and PVDF triboelectric effect to measure heart rate, blood pressure, and breathing patterns. Three constant pressures were measured in this study. At a constant pressure of stage 1, the NHMS produces an average AC of …

Medical Image Segmentation Using Machine Learning, Masoud Khani

Theses and Dissertations

Image segmentation is the most crucial step in image processing and analysis. It can divide an image into meaningfully descriptive components or pathological structures. The result of the image division helps analyze images and classify objects. Therefore, getting the most accurate segmented image is essential, especially in medical images. Segmentation methods can be divided into three categories: manual, semiautomatic, and automatic. Manual is the most general and straightforward approach. Manual segmentation is not only time-consuming but also is imprecise. However, automatic image segmentation techniques, such as thresholding and edge detection, are not accurate in the presence of artifacts like noise …

Development Of A 7 Dof Exoskeleton Robot For Rehabilitation Of Lower Extremity, Sk Hasan

Theses and Dissertations

The World Health Organization reports that worldwide about 1 billion people have some form ofdisability. Of these, 110-190 million people have significant difficulties in functioning (mainly upper and lower extremity disability) independently. The major causes of human lower extremity disability include stroke, trauma, spinal cord injuries, and muscular dystrophy. Every 40 seconds, someone in the United States has a stroke. A statistic shows that approximately 65% of post-stroke patients suffer lower extremity impairment. Rehabilitation programs are the main method to promote functional recovery in disabled individuals. The conventional therapeutic approach requires a long commitment from a therapist or a clinician. …

Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Nebulizer-Based Systems To Improve Pharmaceutical Aerosol Delivery To The Lungs, Benjamin M. Spence

Theses and Dissertations

Combining vibrating mesh nebulizers with additional new technologies leads to substantial improvements in pharmaceutical aerosol delivery to the lungs across therapeutic administration methods. In this dissertation, streamlined components, aerosol administration synchronization, and/or Excipient Enhanced Growth (EEG) technologies were utilized to develop and test several novel devices and aerosol delivery systems. The first focus of this work was to improve the poor delivery efficiency, e.g., 3.6% of nominal dose (Dugernier et al. 2017), of aerosolized medication administration to adult human subjects concurrent with high flow nasal cannula (HFNC) therapy, a form of continuous-flow non-invasive ventilation (NIV). The developed Low-Volume Mixer-Heater (LVMH) …

A Discrete-Event Simulation Approach For Modeling Human Body Glucose Metabolism, Buket Aydas

Theses and Dissertations

This dissertation describes CarbMetSim (Carbohydrate Metabolism Simulator), a discrete-event simulator that tracks the blood glucose level of a person in response to a timed sequence of diet and exercise activities. CarbMetSim implements broader aspects of carbohydrate metabolism in human beings with the objective of capturing the average impact of various diet/exercise activities on the blood glucose level. Key organs (stomach, intestine, portal vein, liver, kidney, muscles, adipose tissue, brain and heart) are implemented to the extent necessary to capture their impact on the production and consumption of glucose. Key metabolic pathways (glucose oxidation, glycolysis and gluconeogenesis) are accounted for by …

Design And Basic Verification Of A Discrete Event Simulator For Glucose Metabolism In Human Beings, Elizabeth Andrews

Theses and Dissertations

This thesis describes the design and basic verification of a discrete event simulator for glucose metabolism in human beings. The simulator implements the glucose metabolism related behavior of various organs in the human body and tracks the blood plasma glucose level as the human body goes through a sequence of diet and exercise events. The simulator can mimic insulin resistance in various organs as well as the loss of insulin production in the pancreas and the adverse impact of these changes on the metabolic behavior of various organs. Thus, the simulator can serve as a model for people with diabetes. …

Quantitative Optical Studies Of Oxidative Stress In Rodent Models Of Eye And Lung Injuries, Zahra Ghanian

Theses and Dissertations

Optical imaging techniques have emerged as essential tools for reliable assessment of organ structure, biochemistry, and metabolic function. The recognition of metabolic markers for disease diagnosis has rekindled significant interest in the development of optical methods to measure the metabolism of the organ.

The objective of my research was to employ optical imaging tools and to implement signal and image processing techniques capable of quantifying cellular metabolism for the diagnosis of diseases in human organs such as eyes and lungs. To accomplish this goal, three different tools, cryoimager, fluorescent microscope, and optical coherence tomography system were utilized to study the …

Simulating The Impact Of Diet And Exercise On Blood Glucose Level For People With Type 2 Diabetes, Md Abdul Halim Sarkar

Theses and Dissertations

This thesis describes the design of a discrete-event simulator that tracks the blood glucose level and glycated hemoglobin level as a person goes through her daily diet and exercise activities. This thesis also reports results of simulations performed using the simulator that show the changes in blood glucose level and glycated hemoglobin level as a diabetic person makes specific changes to her diet/exercise routine.

Scattering Correction Methods Of Infrared Spectra Using Graphics Processing Units, Asher Imtiaz

Theses and Dissertations

Fourier transform infrared (FTIR) microspectroscopy has been used for many years as a technique that provides distinctive structure-specific infrared spectra for a wide range of materials (e.g., biological (tissues, cells, bacteria, viruses), polymers, energy related, composites, minerals). The mid-infrared radiation can strongly scatter from distinct particles, with diameters ranging between 2-20 micrometer. Transmission measurements of samples (approximately 100 micrometers x 100 micrometers x 10 micrometers) with distinct particles. will be dominated by this scattering (Mie scattering). The scattering distorts the measured spectra, and the absorption spectra appear different from pure absorbance spectra. This thesis presents development and implementation of two …

Femtosecond Laser Beam Propagation Through Corneal Tissue: Evaluation Of Therapeutic Laser-Stimulated Second And Third-Harmonic Generation, William R. Calhoun Iii

Theses and Dissertations

One of the most recent advancements in laser technology is the development of ultrashort pulsed femtosecond lasers (FSLs). FSLs are improving many fields due to their unique extreme precision, low energy and ablation characteristics. In the area of laser medicine, ophthalmic surgeries have seen very promising developments. Some of the most commonly performed surgical operations in the world, including laser-assisted in-situ keratomileusis (LASIK), lens replacement (cataract surgery), and keratoplasty (cornea transplant), now employ FSLs for their unique abilities that lead to improved clinical outcome and patient satisfaction.

The application of FSLs in medical therapeutics is a recent development, and although …

Monitoring Changes In Hemodynamics Following Optogenetic Stimulation, Seth Thomas Frye

Theses and Dissertations

The brain is composed of billions of neurons, all of which connected through a vast network. After years of study and applications of different technologies and techniques, there are still more questions than answers when it comes to the fundamental functions of the brain. This project aims to provide a new tool which can be used to gain a better understanding of the fundamental mechanisms that govern neurological processes inside the brain. In order for neural networks to operate, blood has to be supplied through neighboring blood vessels. As such, the increase or decrease in the blood supply has been …

Citefinder: A System To Find And Rank Medical Citations, Seyed Soheil Moosavinasab

Theses and Dissertations

This thesis presents CiteFinder, a system to find relevant citations for clinicians' written content. Inclusion of citations for clinical information content makes the content more reliable through the provision of scientific articles as references, and enables clinicians to easily update their written content using new information. The proposed approach splits the content into sentences, identifies the sentences that need to be supported with citations by applying classification algorithms, and uses information retrieval and ranking techniques to extract and rank relevant citations from MEDLINE for any given sentence. Additionally, this system extracts snippets from the retrieved articles. We assessed our approach …

Extracting The Structure And Conformations Of Biological Entities From Large Datasets, Ali Dashti

Theses and Dissertations

In biology, structure determines function, which often proceeds via changes in conformation. Efficient means for determining structure exist, but mapping conformations continue to present a serious challenge. Single-particles approaches, such as cryogenic electron microscopy (cryo-EM) and emerging "diffract & destroy" X-ray techniques are, in principle, ideally positioned to overcome these challenges. But the algorithmic ability to extract information from large heterogeneous datasets consisting of "unsorted" snapshots - each emanating from an unknown orientation of an object in an unknown conformation - remains elusive.

It is the objective of this thesis to describe and validate a powerful suite of manifold-based algorithms …

Extraction And Classification Of Drug-Drug Interaction From Biomedical Text Using A Two-Stage Classifier, Majid Rastegar-Mojarad

Theses and Dissertations

One of the critical causes of medical errors is Drug-Drug interaction (DDI), which occurs when one drug increases or decreases the effect of another drug. We propose a machine learning system to extract and classify drug-drug interactions from the biomedical literature, using the annotated corpus from the DDIExtraction-2013 shared task challenge. Our approach applies a two-stage classifier to handle the highly unbalanced class distribution in the corpus. The first stage is designed for binary classification of drug pairs as interacting or non-interacting, and the second stage for further classification of interacting pairs into one of four interacting types: advise, effect, …

Efficient Computation Of K-Nearest Neighbor Graphs For Large High-Dimensional Data Sets On Gpu Clusters, Ali Dashti

Theses and Dissertations

The k-Nearest Neighbor Graph (k-NNG) and the related k-Nearest Neighbor (k-NN) methods have a wide variety of applications in areas such as bioinformatics, machine learning, data mining, clustering analysis, and pattern recognition. Our application of interest is manifold embedding. Due to the large dimensionality of the input data (<15k), spatial subdivision based techniques such OBBs, k-d tree, BSP etc., are not viable. The only alternative is the brute-force search, which has two distinct parts. The first finds distances between individual vectors in the corpus based on a pre-defined metric. Given the distance matrix, the second step selects k nearest neighbors for each member of the query data set.

This thesis presents the development and implementation of a distributed exact k-Nearest Neighbor Graph (k-NNG) construction method. The proposed method uses Graphics Processing Units (GPUs) and exploits multiple levels of parallelism for distributed computational systems using GPUs. It is scalable for different cluster sizes, with each compute node in the cluster …

Optical Cryoimaging Of Celular Redox In Kidneys From Diabetic Mice, Sepideh Maleki

Theses and Dissertations

Diabetic Nephropathy (DN) is the major single cause of end stage renal diseases (ESRD) in the United States. Diabetes is the third leading fatal disorder after cancer and heart disease. It is affecting 8.3% of the residents of the United States, with a total healthcare cost of $174 billion/yr by 2010.

There currently exists a need for a sensitive and specific diagnosis for temporal detection of oxidative stress (OS) in cellular metabolic levels, which plays an early role in the development of DN. The objective of this research is to use a fluorescence optical imaging technique in order to delineate …

Quantitative Object Reconstruction Using Abel Transform Tomography And Mixed Variable Optimization, Kevin R. O'Reilly

Theses and Dissertations

Researchers at the Los Alamos National Laboratory (LANL) are interested in quantitatively reconstructing an object using Abel transform x-ray tomography. Specifically, they obtain a radiograph by x-raying an object and attempt to quantitatively determine the number and types of materials and the thicknesses of each material layer. Their current methodologies either fail to provide a quantitative description of the object or are generally too slow to be useful in practice. As an alternative, the problem is modeled here as a mixed variable programming (MVP) problem, in which some variables are nonnumeric and for which no derivative information is available. The …

Pulse Coupled Neural Networks For The Segmentation Of Magnetic Resonance Brain Images, Shane L. Abrahamson

Theses and Dissertations

This research develops an automated method for segmenting Magnetic Resonance (MR) brain images based on Pulse Coupled Neural Networks (PCNN). MR brain image segmentation has proven difficult, primarily due to scanning artifacts such as interscan and intrascan intensity inhomogeneities. The method developed and presented here uses a PCNN to both filter and segment MR brain images. The technique begins by preprocessing images with a PCNN filter to reduce scanning artifacts. Images are then contrast enhanced via histogram equalization. Finally, a PCNN is used to segment the images to arrive at the final result. Modifications to the original PCNN model are …

Evaluation Of Segmentation For Bone Structures In 3d Rendering Of Ultrasound Residual Limb Images, Min C. Baker

Theses and Dissertations

Prosthetists today widely practice manual socket fitting, which produces subjective, inconsistent results. To address this problem, the Computerized Anthropometry Research and Design (CARD) Laboratory is developing a computer-aided socket design system that acquires ultrasound datasets of an amputee's residual limb, creates a 3D model, and helps identify load- bearing and pressure-relief areas. This research project focuses on providing 3D visualization of a residual limb to support the CARD Laboratory's efforts. Creating the 3D model of the skin and two bone contours requires two major steps: segmentation to identify the objects of interest and a surface tracking algorithm to generate the …

Computer-Aided Diagnosis Of Mammographic Masses, William E. Polakowski

Theses and Dissertations

A new Model-Based Vision algorithm was developed to find possibly cancerous regions of interest (ROIs) in digitized mammograms and to correctly identify the malignant masses. This work has shown a sensitivity of 92 percent for locating malignant ROIs. The database contained 272 images (12 bit, 1OO microns) with 36 malignant and 53 benign mass images. Of the 53 biopsied benign cases, 74 percent were correctly classified. The Focus of Attention (segmentation) Module algorithm used a physiologically motivated Difference of Gaussians (DoG) filter to highlight mass-like regions in the mammogram. The Index Module labeled the regions by their hypothesized class: large …

Development Of A Mammographic Image Processing Environment Using Matlab, John L. Kelley

Theses and Dissertations

Breast cancer is a disease that accounts for a disturbingly large number of deaths in females each year. Its prevalence is a topic of concern to all of us since it can affect our families, friends, and coworkers. Although mammographic screening is the most effective method currently available for the early detection of breast cancer, it is far from being an infallible procedure. Mammographic reading is error prone, partly because of the complexity of the task and partly because of the variability in human performance. Computers offer high reproducibility, and when used as an adjunct by the radiologist, may improve …

The Afit Multielectrode Array For Neural Recording And Simulation: Design, Testing, And Encapsulation, James R. Reid Jr

Theses and Dissertations

A two-dimensional, X-Y addressable, multiplexed array of 256 electrodes (16 x 16) has been fabricated using conventional semiconductor processing techniques. The individual electrodes are 16O microns x 160 microns, approximating the size of the cortical columns; the overall array size is 3910 microns x 3910 microns. The array has been fitted to a chronically implantable package and tested for several days in a simulated neural environment. EEG-like data were collected successfully from individual electrodes in the array. This array improves on a previous design of a 16 electrode (4 x 4) array that was chronically implanted on the cortex of …