Biomedical Commons^™

Design & Delivery Of Automated Winston-Lutz Test For Isocentric & Off-Axis Delivery Stability Utilizing Truebeam Developer Mode & Electronic Portal Imaging Device, Mahmoud Mohammad Yaqoub

UNLV Theses, Dissertations, Professional Papers, and Capstones

The uncertainties in treatment delivery cannot be ignored in radiation therapy. Thus, the quality assurance QA tests are very important task of the medical physicist in clinical practice. Assuring the coincidence between the mechanical isocenter of the Linear Accelerator (LINAC) and its radiation beams isocenter is one of the most important qualities need to be tested, and the Winston Lust (WL) test is the

most popular technique to perform this task, especially for the treatment modalities which need high precision in beam delivery such as the stereotactic radiosurgery/stereotactic body radiotherapy (SRS/SBRT). The linear accelerator-based SRS/SBRT is a well-established method in …

Phase Unwrapping Of 4d-Flow Mri Data With Graph Cuts., Andrew Justice

Electronic Theses and Dissertations

A common issue when measuring velocity utilizing 4D flow magnetic resonance imaging (MRI) is aliasing that occurs because of a low velocity encoding parameter (VENC). Aliasing can be avoided if the velocity encoding parameter is set above the largest velocity quantity. However, when this is done the velocity to noise ratio is lowered less detail is acquired in the image. Thusly, it is sometimes desirable to have a below the maximum velocity to acquire higher quality data.

Consequently, an efficient and robust algorithm is needed to unwrap the aliased data. This paper proposes an iterative graph cuts algorithm to perform …

Longitudinal Tracking Of Physiological State With Electromyographic Signals., Robert Warren Stallard

Electronic Theses and Dissertations

Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A …

Segmentation, Tracking, And Kinematics Of Lung Parenchyma And Lung Tumors From 4d Ct With Application To Radiation Treatment Planning., Jungwon Cha

Electronic Theses and Dissertations

This thesis is concerned with development of techniques for efficient computerized analysis of 4-D CT data. The goal is to have a highly automated approach to segmentation of the lung boundary and lung nodules inside the lung. The determination of exact lung tumor location over space and time by image segmentation is an essential step to track thoracic malignancies. Accurate image segmentation helps clinical experts examine the anatomy and structure and determine the disease progress. Since 4-D CT provides structural and anatomical information during tidal breathing, we use the same data to also measure mechanical properties related to deformation of …

Evaluation Of Terahertz Imaging For Breast Cancer Detection Using Image Morphing, Tanny Andrea Chavez Esparza

Graduate Theses and Dissertations

This thesis proposes the use of a mesh morphing algorithm for the quantitative evaluation of terahertz (THz) images. This work differs from traditional evaluation methods based on qualitative evaluation because it provides a fair and quantitative measurement of the THz imaging system's performance. The objective of the algorithm is to match the alignment, shape, and resolution of the THz and reference pathology images. Therefore, the proposed morphing method provides a pathology reference for a pixel-by-pixel evaluation of the region classification in the THz image. To achieve this, the morphing algorithm aligns the images using the Pearson's correlation coefficient and reshapes …

Detecting Suicide Risk From Wristworn Activity Tracker Data Using Machine Learning Approaches, Pallavi Atluri

Electrical Engineering Theses

Suicide is a prevalent cause of death worldwide and depression is a primary concern of many suicidal acts. It is possible that an individual during depression never has any suicidal thoughts at all. On the other hand, some individuals in stable condition with no apparent symptoms of depression feel urges to commit suicide (suicidal ideation). Many such individuals never let anyone know what they are feeling or planning. Suicidal ideation considered an important precursor to suicidal acts.

Detecting the suicide risk in individuals with mood disorders is a major challenge. The current clinical practice to assess suicide risk in these …

Image Processing Applications In Real Life: 2d Fragmented Image And Document Reassembly And Frequency Division Multiplexed Imaging, Houman Kamran Habibkhani

LSU Doctoral Dissertations

In this era of modern technology, image processing is one the most studied disciplines of signal processing and its applications can be found in every aspect of our daily life. In this work three main applications for image processing has been studied.

In chapter 1, frequency division multiplexed imaging (FDMI), a novel idea in the field of computational photography, has been introduced. Using FDMI, multiple images are captured simultaneously in a single shot and can later be extracted from the multiplexed image. This is achieved by spatially modulating the images so that they are placed at different locations in the …

3d Bioprinting Systems For The Study Of Mammary Development And Tumorigenesis, John Reid

Electrical & Computer Engineering Theses & Dissertations

Understanding the microenvironmental factors that control cell function, differentiation, and stem cell renewal represent the forefront of developmental and cancer biology. To accurately recreate and model these dynamic interactions in vitro requires both precision-controlled deposition of multiple cell types and well-defined three-dimensional (3D) extracellular matrix (ECM). To achieve this goal, we hypothesized that accessible bioprinting technology would eliminate the experimental inconsistency and random cell-organoid formation associated with manual cell-matrix embedding techniques commonly used for 3D, in vitro cell cultures. The first objective of this study was to adapt a commercially-available, 3D printer into a 3D bioprinter. Goal-based computer simulations were …

Deep Gaze Velocity Analysis During Mammographic Reading For Biometric Identification Of Radiologists, Hong-Jun Yoon, Folami Alamudun, Kathy Hudson, Garnetta Morin-Ducote, Georgia Tourassi

Journal of Human Performance in Extreme Environments

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that …

Chronic Risk And Disease Management Model Using Structured Query Language And Predictive Analysis, Mamata Ojha

Electronic Theses and Dissertations

Individuals with chronic conditions are the ones who use health care most frequently and more than 50% of top ten causes of death are chronic diseases in United States and these members always have health high risk scores. In the field of population health management, identifying high risk members is very important in terms of patient health care, disease management and cost management. Disease management program is very effective way of monitoring and preventing chronic disease and health related complications and risk management allows physicians and healthcare companies to reduce patient’s health risk, help identifying members for care/disease management along …

Upregulation Of Dna Sensors In B16.F10 Melanoma Spheroid Cells After Electrotransfer Of Pdna, Katarina Znidar, Masa Bosnjak, Tanja Jesenko, Loree C. Heller, Maja Cemazar

Bioelectrics Publications

Increased expression of cytosolic DNA sensors, a category of pattern recognition receptor, after control plasmid DNA electrotransfer was observed in our previous studies on B16.F10 murine melanoma cells. This expression was correlated with the upregulation of proinflammatory cytokines and chemokines and was associated with cell death. Here, we expanded our research to include the influence of features of cells in a 3-dimensional environment, which better represents the tumors’ organization in vivo. Our results show that lower number of cells were transfected in spheroids compared to 2-dimensional cultures, that growth was delayed after electroporation alone or after electrotransfer of plasmid …

Excitation And Injury Of Adult Ventricular Cardiomyocytes By Nano- To Millisecond Electric Shocks, Iurii Semenov, Sergey Grigoryev, Johanna U. Neuber, Christian W. Zemlin, Olga N. Pakhomova, Maura Casciola, Andrei G. Pakhomov

Bioelectrics Publications

Intense electric shocks of nanosecond (ns) duration can become a new modality for more efficient but safer defibrillation. We extended strength-duration curves for excitation of cardiomyocytes down to 200 ns, and compared electroporative damage by proportionally more intense shocks of different duration. Enzymatically isolated murine, rabbit, and swine adult ventricular cardiomyocytes (VCM) were loaded with a Ca²⁺ indicator Fluo-4 or Fluo-5N and subjected to shocks of increasing amplitude until a Ca²⁺ transient was optically detected. Then, the voltage was increased 5-fold, and the electric cell injury was quantified by the uptake of a membrane permeability marker dye, propidium …

A Scale Space Local Binary Pattern (Sslbp) – Based Feature Extraction Framework To Detect Bones From Knee Mri Scans, Jinyeong Mun

Electronic Theses and Dissertations

The medical industry is currently working on a fully autonomous surgical system, which is considered a novel modality to go beyond technical limitations of conventional surgery. In order to apply an autonomous surgical system to knees, one of the primarily responsible areas for supporting the total weight of human body, accurate segmentation of bones from knee Magnetic Resonance Imaging (MRI) scans plays a crucial role. In this paper, we propose employing the Scale Space Local Binary Pattern (SSLBP) feature extraction, a variant of local binary pattern extractions, for detecting bones from knee images. The proposed methods consist of two phases. …

Adaptive Interventions Treatment Modelling And Regimen Optimization Using Sequential Multiple Assignment Randomized Trials (Smart) And Q-Learning, Abiral Baniya

Electronic Theses and Dissertations

Nowadays, pharmacological practices are focused on a single best treatment to treat a disease which sounds impractical as the same treatment may not work the same way for every patient. Thus, there is a need of shift towards more patient-centric rather than disease-centric approach, in which personal characteristics of a patient or biomarkers are used to determine the tailored optimal treatment. The “one size fits all” concept is contradicted by research area of personalized medicine. The Sequential Multiple Assignment Randomized Trial (SMART) is a multi-stage trials to inform the development of dynamic treatment regimens (DTR’s). In SMART, a subject is …

Eeg And Emg Sensorimotor Measurements To Assess Proprioception Following Acl Reconstruction, Teagan Frances Northrup

Honors Theses and Capstones

The Anterior Cruciate Ligament (ACL) is the primary source of rotational stability in the knee by preventing the tibia from sliding in front of the femur. When the ACL is torn, it typically must be repaired through reconstructive surgery which results in proprioceptive deficiencies in the knee. Proprioception plays an important role in understanding where one’s knee is in space, sensing movement and reacting accordingly. This study examines an alternative method of measuring proprioceptive responses to a stimulus (motion) by using electromyogram (EMG) and electroencephalogram (EEG) signals to observe muscle and brain activity. Two participants (one with an ACL reconstruction …

An Investigation Of Argumentation Theory For The Prediction Of Survival In Elderly Using Biomarkers, Lucas Middeldorf Rizzo, Ljiljana Majnaric, Pierpaolo Dondio, Luca Longo

Conference papers

Research on the discovery, classification and validation of biological markers, or biomarkers, have grown extensively in the last decades. Newfound and correctly validated biomarkers have great potential as prognostic and diagnostic indicators, but present a complex relationship with pertinent endpoints such as survival or other diseases manifestations. This research proposes the use of computational argumentation theory as a starting point for the resolution of this problem for cases in which a large amount of data is unavailable. A knowledge-base containing 51 different biomarkers and their association with mortality risks in elderly was provided by a clinician. It was applied for …

Interactive Clinical Event Pattern Mining And Visualization Using Insurance Claims Data, Zhenhui Piao

Theses and Dissertations--Computer Science

With exponential growth on a daily basis, there is potentially valuable information hidden in complex electronic medical records (EMR) systems. In this thesis, several efficient data mining algorithms were explored to discover hidden knowledge in insurance claims data. The first aim was to cluster three levels of information overload(IO) groups among chronic rheumatic disease (CRD) patient groups based on their clinical events extracted from insurance claims data. The second aim was to discover hidden patterns using three renowned pattern mining algorithms: Apriori, frequent pattern growth(FP-Growth), and sequential pattern discovery using equivalence classes(SPADE). The SPADE algorithm was found to be the …

Associative Learning In Biochemical Networks, Yasmin S. Sepulveda

REU Final Reports

Emerging evidence suggests that biochemical networks can be modeled by exploiting their ability to learn through associative learning. This type of learning in biomolecular structures gives it a the advantage to be able to be computationally model, and condition. Associative learning in biochemical networks is a developing area of study that once understood, can further develop diagnostic applications, and be used as tools for data analysis. Although it is a open ended project the motive of this research was to find the the best method of association learning being used in current work. After reading current work three associative learning …

Deciphering The Rules Of Cell-To-Cell Coupling By Molecular Modeling And Simulation, Linda D. Lee

REU Final Reports

Intercellular communication is vital for quick adjustments and maintenance for cell function and development. Gap junctions are membranes channel proteins that enable this direct communication between adjacent cells throughout the body. The compatibility of connexins (Cx), which make up a gap junction, determines whether a gap junction can form. Though many studies show which connexins are compatible, the molecular basis is not known (Bai & Wang, 2014). Through computational modeling, we identify the residues that energetically contribute most favorably at the docking interface of homotypic and heterotypic combinations of Cx43, Cx46, and Cx50 gap junctions. However, due to instability of …

Muscle Activity-Driven Green-Oriented Random Number Generation Mechanism To Secure Wbsn Wearable Device Communications, Yuanlong Cao, Guanghe Zhang, Fanghua Liu, Ilsun You, Guanglou Zheng, Oluwarotimi W. Samuel, Shixiong Chen

Research outputs 2014 to 2021

Wireless body sensor networks (WBSNs) mostly consist of low-cost sensor nodes and implanted devices which generally have extremely limited capability of computations and energy capabilities. Hence, traditional security protocols and privacy enhancing technologies are not applicable to the WBSNs since their computations and cryptographic primitives are normally exceedingly complicated. Nowadays, mobile wearable and wireless muscle-computer interfaces have been integrated with the WBSN sensors for various applications such as rehabilitation, sports, entertainment, and healthcare. In this paper, we propose MGRNG, a novel muscle activity-driven green-oriented random number generation mechanism which uses the human muscle activity as green energy resource to generate …