Bioimaging and Biomedical Optics Commons^™

Influence Of Motion Artifacts On Instantaneous Frequency Based Heart Rate Variability Of Arterial Pulse Signals Measured By A Tactile Sensor: An Analytical Study, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

Objective: Heart rate variability (HRV), quantified via the instantaneous frequency (IF) of arterial pulse signals, is a key indicator of cardiovascular (CV) regulation. This study presents a theoretical and experimental investigation of how motion artifacts (MA) influence IF-and thus HRVmeasured with a tactile sensor, emphasizing underlying mechanisms rather than population-level validation. Approach: Building on a previously developed single-degree-of-freedom (SDOF) model of the tissue-contact-sensor (TCS) stack between the sensor and the artery, MA is represented as low-frequency baseline drift and time-varying system parameters (TVSP) of the TCS stack. Analytical expressions quantify the effect of MA on the IF of each harmonic …

Fill The Void - An Introductory Guide For Learning Medical Imaging, Reid Faith, Peter Gahan, Adam Trusty, Aleksandr Zyskin, Chukwuka Ukekwe, Denise Nchinda, Amanda K. Burbage, Alberto E. Musto

EVMS School of Health Professions Faculty Publications

No standardized, comprehensive approach for radiological image interpretation exists among medical schools in the United States of America. In order to rectify this, we outline a model aimed to help improve how medical imaging can be taught in medical schools. The VOID (Visualization, Orientation/Observation, Identification, Description) guide is a proposed universal, stepwise approach to medical image identification, interpretation, and communication. This structured guideline is aimed at building a foundation for students in early medical education. The VOID was applied to CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and x-ray modalities as integrated sessions for medical students during human anatomy laboratory. …

Implementation And Clinical Utility Of Ultra-Low-Field Portable Magnetic Resonance Imaging For Postprocedural Neurological Evaluation In Ambulatory Neurosurgery: Illustrative Cases, Devan Patel, Vinay Jaikumar, Taysia P. T. Morioka, Laz Rifkin, Kenneth S. Jacoby, Jaims Lim, Anais Andrade, Aimee C. Degaetano, Pui Man Rosalind Lai, Elad I. Levy

EVMS School of Health Professions Faculty Publications

BACKGROUND

Elective endovascular neurosurgical procedures are increasingly performed in ambulatory neurosurgery centers, enabled by advances in catheter technology, safety of conscious sedation, and refined patient selection. Although complication rates are low, rapid evaluation of postprocedural neurological deficits remains critical. Conventional MRI is often impractical in outpatient or procedural settings, whereas ultra-low-field portable MRI (ULF-pMRI) systems (such as Swoop) allow bedside imaging with favorable diagnostic performance.

OBSERVATIONS

Two women in their 60s developed acute neurological deficits at an ambulatory neurosurgery center (ANSC) after diagnostic cerebral angiography in one case and elective internal carotid artery flow diversion in the other. In both …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

On The Scalability Of Anisotropic Mesh Adaptation On Distributed And Shared Memory Architectures For Numerical Approximations, Kevin Mark Garner Jr.

Computer Science Theses & Dissertations

Mesh generation is a critical component in numerical approximations of Partial Differential Equations (PDEs). One such example includes Computational Fluid Dynamics (CFD), as CFD simulations in turn are crucial for applications in many industries, such as personalized healthcare and the design of aerospace vehicles. Generating high quality meshes for large-scale CFD problems presents a significant bottleneck in the CFD workflow. This dissertation proposes “fast,” parallel 3D mesh generation methodologies that are designed to leverage the concurrency offered by emerging High-Performance Computing (HPC) architectures. First, a distributed memory method is presented that integrates a sequential state-of-the-art isotropic, advancing front local reconnection-based …

Ac Electric Fields Manipulate And Concentrate Dna Molecules On Electrodes, Akila Wijesinghe, Dharmakeerthi Nawarathna

Graduate Student Government Association Research Conference

Point-of-care (POC) electric field-based biosensors have emerged as a promising tool to detect early cancer biomarkers such as circulating tumor DNA (ctDNA), microRNA (miRNA), and proteins. To be effective in screening in clinical settings, these biosensors must be simple and easy to use. In this study, we have studied the manipulation of short DNA molecules suspended in a sessile drop to achieve this goal. Alternative current (AC) electric fields were used to polarize DNA molecules and produce dielectrophoretic (DEP) force on DNA molecules. DEP force is used to manipulate polarized DNA molecules towards the higher electric field gradients (toward the …

Automation To Autonomy: Temporal Dynamics Of Trust And Visual Attention Allocation Did Not Evolve, Tetsuya Sato, Eric Chancey, Yusuke Yamani

Psychology Faculty Publications

Emerging work environments are expected to implement autonomy that performs various functions without human input. Previous works has shown that trust in automation is negatively correlated with visual attention allocation, indicating that trust is a dynamic construct. Moreover, trust in automation and trust in autonomy appears to evolve in similar ways. However, recent work has demonstrated differences between trust in automation and trust in autonomy within Kaber’s (2018) theoretical framework (Sato et al., 2023b). Yet, it is uncertain whether the development of trust and visual attention allocation differs between automation and autonomy. The present study examined the temporal dynamics of …

A Method For Ultrasound Servo Tracking For Puncture Needle, Shitong Ye, Bo Yang, Hao Quan, Shan Liu, Minyu Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

Computer-aided surgical navigation technology helps and guides doctors to complete the operation smoothly, which simulates the whole surgical environment with computer technology, and then visualizes the whole operation link in three dimensions. At present, common image-guided surgical techniques such as computed tomography (CT) and X-ray imaging (X-ray) will cause radiation damage to the human body during the imaging process. To address this, we propose a novel Extended Kalman filter-based model that tracks the puncture needle-point using an ultrasound probe. To address the limitations of Kalman filtering methods based on position and velocity, our method of Kalman filtering uses the position …

Adaptive Fusion Neural Networks For Sparse-Angle X-Ray 3d Reconstruction, Shaoyong Hong, Bo Yang, Yan Chen, Hao Quan, Shan Liu, Minyi Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion, thereby improving …

Real-Time Navigation System For Breast Cancer Surgery With Pre- And Intra-Operative Imaging Using Neural Networks, Motaz Alqaoud

Biomedical Engineering Theses & Dissertations

Breast cancer is one of the most frequently diagnosed malignancies in women worldwide, necessitating precise diagnosis and treatment. Breast-conserving surgery (BCS) is the primary treatment for nonpalpable cases, yet current approaches often lack accuracy due to the absence of real-time 3D imaging during surgery. This limitation impairs surgeons’ ability to visualize tumor locations, compromising outcomes and potentially leading to repeat surgeries with higher risks, undesirable cosmetic results, increased costs, and, in some cases, mastectomy. Thus, there is a critical need for a navigation system to facilitate accurate tumor excision in nonpalpable breast cancer through real-time patient-specific 3D tracking.

This study …

Plasma Protein Signatures Of Adult Asthma, Gordon J. Smilnak, Yura Lee, Abhijnan Chattopadhyay, Annah B. Wyss, Julie D. White, Sinjini Sikdar, Jianping Jin, Andrew J. Grant, Alison A. Motsinger-Reif, Jian-Liang Li, Mikyeong Lee, Bing Yu, Stephanie J. London

Mathematics & Statistics Faculty Publications

Background: Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma.

Methods: Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with …

Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …

Enhancement Of Deep Learning Protein Structure Prediction, Ruoming Shen

Modeling, Simulation and Visualization Student Capstone Conference

Protein modeling is a rapidly expanding field with valuable applications in the pharmaceutical industry. Accurate protein structure prediction facilitates drug design, as extensive knowledge about the atomic structure of a given protein enables scientists to target that protein in the human body. However, protein structure identification in certain types of protein images remains challenging, with medium resolution cryogenic electron microscopy (cryo-EM) protein density maps particularly difficult to analyze. Recent advancements in computational methods, namely deep learning, have improved protein modeling. To maximize its accuracy, a deep learning model requires copious amounts of up-to-date training data.

This project explores DeepSSETracer, a …

Evaluating Human Eye Features For Objective Measure Of Working Memory Capacity, Yasasi Abeysinghe, Enkelejda Kasneci (Ed.), Frederick Shic (Ed.), Mohamed Khamis (Ed.)

Computer Science Faculty Publications

Eye tracking measures can provide means to understand the underlying development of human working memory. In this study, we propose to develop machine learning algorithms to find an objective relationship between human eye movements via oculomotor plant and their working memory capacity, which determines subjective cognitive load. Here we evaluate oculomotor plant features extracted from saccadic eye movements, traditional positional gaze metrics, and advanced eye metrics such as ambient/focal coefficient , gaze transition entropy, low/high index of pupillary activity (LHIPA), and real-time index of pupillary activity (RIPA). This paper outlines the proposed approach of evaluating eye movements for obtaining an …

Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter

Electrical & Computer Engineering Faculty Publications

Refractive index (RI) sensors are of great interest for label-free optical biosensing. A tapered optical fiber (TOF) RI sensor with micron-sized waist diameters can dramatically enhance sensor sensitivity by reducing the mode volume over a long distance. Here, a simple and fast method is used to fabricate highly sensitive refractive index sensors based on localized surface plasmon resonance (LSPR). Two TOFs (l = 5 mm) with waist diameters of 5 µm and 12 µm demonstrated sensitivity enhancement at λ = 1559 nm for glucose sensing (5-45 wt%) at room temperature. The optical power transmission decreased with increasing glucose concentration due …

Development And Validation Of A Three-Dimensional Optical Imaging System For Chest Wall Deformity Measurement, Nahom Kidane

Electrical & Computer Engineering Theses & Dissertations

Congenital chest wall deformities (CWD) are malformations of the thoracic cage that become more pronounced during early adolescence. Pectus excavatum (PE) is the most common CWD, characterized by an inward depression of the sternum and adjacent costal cartilage. A cross-sectional computed tomography (CT) image is mainly used to calculate the chest thoracic indices. Physicians use the indices to quantify PE deformity, prescribe surgical or non-surgical therapies, and evaluate treatment outcomes. However, the use of CT is increasingly causing physicians to be concerned about the radiation doses administered to young patients. Furthermore, radiographic indices are an unsafe and expensive method of …

Analysis Of An Existing Method In Refinement Of Protein Structure Predictions Using Cryo-Em Images, Maytha Alshammari, Jing He, Willy Wriggers, Jiangwen Sun

College of Sciences Posters

Protein structure prediction produces atomic models from its amino acid sequence. Three-dimensional structures are important for understanding the function mechanism of proteins. Knowing the structure of a given protein is crucial in drug development design of novel enzymes. AlphaFold2 is a protein structure prediction tool with good performance in recent CASP competitions. Phenix is a tool for determination of a protein structure from a high-resolution 3D molecular image. Recent development of Phenix shows that it is capable to refine predicted models from AlphaFold2, specifically the poorly predicted regions, by incorporating information from the 3D image of the protein. The goal …

Two-Stage Transfer Learning For Facial Expression Classification In Children, Gregory Hubbard, Megan Witherow, Khan Iftekharuddin

Undergraduate Research Symposium

Studying facial expressions can provide insight into the development of social skills in children and provide support to individuals with developmental disorders. In afflicted individuals, such as children with Autism Spectrum Disorder (ASD), atypical interpretations of facial expressions are well-documented. In computer vision, many popular and state-of-the-art deep learning architectures (VGG16, EfficientNet, ResNet, etc.) are readily available with pre-trained weights for general object recognition. Transfer learning utilizes these pre-trained models to improve generalization on a new task. In this project, transfer learning is implemented to leverage the pretrained model (general object recognition) on facial expression classification. Though this method, the …

Generation, Analysis, And Evaluation Of Patient-Specific, Osteoligamentous, Spine Meshes, Austin R. Tapp

Biomedical Engineering Theses & Dissertations

Scoliosis, an abnormal curvature of the spine, is traditionally corrected with bracing treatments or by a highly invasive posterior spinal fusion (PSF) operation. These correction strategies are constrained by current imaging modalities, which fail to elucidate the soft tissue anatomy that is known to play a critical role in spinal stiffness and overall structure. Osteoligamentous segmentations of the spinal column offer a foundation for downstream finite element (FE) studies seeking to optimize bracing treatments or determine ideal surgical approaches.

This thesis presents methods for automatically and semi-automatically segmenting vertebrae and surrounding soft tissues of the spinal column using X-ray computed …

Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to …

Combining Cryo-Em Density Map And Residue Contact For Protein Secondary Structure Topologies, Maytha Alshammari, Jing He

Computer Science Faculty Publications

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set …

Model-Based Approach For Diffuse Glioma Classification, Grading, And Patient Survival Prediction, Zeina A. Shboul

Electrical & Computer Engineering Theses & Dissertations

The work in this dissertation proposes model-based approaches for molecular mutations classification of gliomas, grading based on radiomics features and genomics, and prediction of diffuse gliomas clinical outcome in overall patient survival. Diffuse gliomas are types of Central Nervous System (CNS) brain tumors that account for 25.5% of primary brain and CNS tumors and originate from the supportive glial cells. In the 2016 World Health Organization’s (WHO) criteria for CNS brain tumor, a major reclassification of the diffuse gliomas is presented based on gliomas molecular mutations and the growth behavior. Currently, the status of molecular mutations is determined by obtaining …

Label-Free Microrna Optical Biosensors, Meimei Lai, Gymama Slaughter

Bioelectrics Publications

MicroRNAs (miRNAs) play crucial roles in regulating gene expression. Many studies show that miRNAs have been linked to almost all kinds of disease. In addition, miRNAs are well preserved in a variety of specimens, thereby making them ideal biomarkers for biosensing applications when compared to traditional protein biomarkers. Conventional biosensors for miRNA require fluorescent labeling, which is complicated, time-consuming, laborious, costly, and exhibits low sensitivity. The detection of miRNA remains a big challenge due to their intrinsic properties such as small sizes, low abundance, and high sequence similarity. A label-free biosensor can simplify the assay and enable the direct detection …

Using Feature Extraction From Deep Convolutional Neural Networks For Pathological Image Analysis And Its Visual Interpretability, Wei-Wen Hsu

Electrical & Computer Engineering Theses & Dissertations

This dissertation presents a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer. The deep features being distinguishing in classification from the convolutional neural networks (CNN) are demonstrated in this study to provide comprehensive interpretability for the proposed CAD system using the domain knowledge in pathology. In the experiment, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). Instead of conducting pixel-wise classification (segmentation) into three classes directly, a hierarchical framework with the …

End-To-End Learning Via A Convolutional Neural Network For Cancer Cell Line Classification, Darlington A. Akogo, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Purpose: Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification.

Design/methodology/approach: The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and …

Glioma Grading Using Structural Magnetic Resonance Imaging And Molecular Data, Syed M.S. Reza, Manar D. Samad, Zeina A. Shboul, Karra A. Jones, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

A glioma grading method using conventional structural magnetic resonance image (MRI) and molecular data from patients is proposed. The noninvasive grading of glioma tumors is obtained using multiple radiomic texture features including dynamic texture analysis, multifractal detrended fluctuation analysis, and multiresolution fractal Brownian motion in structural MRI. The proposed method is evaluated using two multicenter MRI datasets: (1) the brain tumor segmentation (BRATS-2017) challenge for high-grade versus low-grade (LG) and (2) the cancer imaging archive (TCIA) repository for glioblastoma (GBM) versus LG glioma grading. The grading performance using MRI is compared with that of digital pathology (DP) images in the …

Tracing Actin Filament Bundles In Three-Dimensional Electron Tomography Density Maps Of Hair Cell Stereocilia, Salim Sazzed, Junha Song, Julio Kovacs, Willi Wriggers, Manfred Auer, Jing He

Computer Science Faculty Publications

Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin …

Computational Modeling For Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, And Grading, Syed Mohammad Shamin Reza

Electrical & Computer Engineering Theses & Dissertations

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology …

Multi-Material Mesh Representation Of Anatomical Structures For Deep Brain Stimulation Planning, Tanweer Rashid

Computational Modeling & Simulation Engineering Theses & Dissertations

The Dual Contouring algorithm (DC) is a grid-based process used to generate surface meshes from volumetric data. However, DC is unable to guarantee 2-manifold and watertight meshes due to the fact that it produces only one vertex for each grid cube. We present a modified Dual Contouring algorithm that is capable of overcoming this limitation. The proposed method decomposes an ambiguous grid cube into a set of tetrahedral cells and uses novel polygon generation rules that produce 2-manifold and watertight surface meshes with good-quality triangles. These meshes, being watertight and 2-manifold, are geometrically correct, and therefore can be used to …