Engineering Commons^™

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 …

Measuring Collagen Arrangement And Its Relationship With Preterm Birth Using Mueller Matrix Polarimetry, Joseph James Chue-Sang

FIU Electronic Theses and Dissertations

Preterm birth (PTB) is defined as delivery prior to 37 weeks of gestation. It is the leading cause of infant death worldwide, responsible for infant neurological disorders, long-term cognitive impairment, as well as chronic health issues involving the auditory, visual, digestive, and respiratory systems. In expectant mothers, causes for PTB can include infection, inflammation, vascular disease, short intervals between pregnancies, multiple gestations and genetic factors. In the U.S., PTB occurs in over 11% of births and at an elevated 18.1% in Miami-Dade County, FL; while in the developing world the incidence of PB is over 15%. Early identification of at-risk …

Resolving Intravoxel White Matter Structures In The Human Brain Using Regularized Regression And Clustering, Andrea Hart, Brianna Smith, Sean Smith, Elijah Sales, Jacqueline Hernandez-Camargo, Yarlin Mayor Garcia, Felix Zhan, Lori Griswold, Brian Dunkelberger, Michael R. Schwob, Sharang Chaudhry, Justin Zhan, Laxmi Gewali, Paul Oh

Computer Science Faculty Research

The human brain is a complex system of neural tissue that varies significantly between individuals. Although the technology that delineates these neural pathways does not currently exist, medical imaging modalities, such as diffusion magnetic resonance imaging (dMRI), can be leveraged for mathematical identification. The purpose of this work is to develop a novel method employing machine learning techniques to determine intravoxel nerve number and direction from dMRI data. The method was tested on multiple synthetic datasets and showed promising estimation accuracy and robustness for multi-nerve systems under a variety of conditions, including highly noisy data and imprecision in parameter assumptions.

Spatial Frequency Domain Imaging Of Short-Wave Infrared Fluorescence For Biomedical Applications, Joseph P. Leonor

ENGS 88 Honors Thesis (AB Students)

Fluorescence imaging has become a standard in many clinical applications, such as tumor and vasculature imaging. One application that is becoming more prominent in cancer treatment is fluorescence-guided surgery (FGS). Currently, FGS allows surgeons the ability to visually navigate tumors and tissue structures intraoperatively. As a result, they can remove tumor more efficiently while maintaining critical structures within the patient, creating better outcomes and lower recovery times. However, background fluorescence and inability to localize depth create challenges when determining resection boundaries.

Different techniques, such as spatially modulating the illumination and imaging at longer light wavelengths, have been developed to accurately …

A Simplified Crossing Fiber Model In Diffusion Weighted Imaging, Sheng Yang, Kaushik Ghosh, Ken Sakaie, Satya S. Sahoo, Sarah J. Ann Carr, Curtis Tatsuoka

Mathematical Sciences Faculty Research

Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and …

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

Biomedical Engineering Or Biomedical Optics: Will The Real Discipline Please Stand Up?, Brian W. Pogue

Dartmouth Scholarship

This editorial reflects on the shape of biomedical engineering as a discipline, and its relation to biomedical optics.

Characterizing Short-Wave Infrared Fluorescence Of Conventional Near-Infrared Fluorophores, Brook K. Byrd, Margaret R. Folaron, Joseph P. Leonor, Rendall R. Strawbridge, Xu Cao, Petr Bruza, Scott C. Davis

Dartmouth Scholarship

The observed behavior of short-wave infrared (SWIR) light in tissue, characterized by relatively low scatter and subdiffuse photon transport, has generated considerable interest for the potential of SWIR imaging to produce high-resolution, subsurface images of fluorescence activity in vivo. These properties have important implications for fluorescence-guided surgery and preclinical biomedical research. Until recently, translational efforts have been impeded by the conventional understanding that fluorescence molecular imaging in the SWIR regime requires custom molecular probes that do not yet have proven safety profiles in humans. However, recent studies have shown that two readily available near-infrared (NIR-I) fluorophores produce measurable SWIR fluorescence, …

Long-Term, Super-Resolution Imaging Of Amyloid Structures Using Transient Amyloid Binding Microscopy, Tianben Ding, Kevin Spehar, Jan Bieschke, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Amyloid fibrils and tangles are signatures of Alzheimer disease, but nanometer-sized aggregation intermediates are hypothesized to be the structures most toxic to neurons. The structures of these oligomers are too small to be resolved by conventional light microscopy. We have developed a simple and versatile method, called transient amyloid binding (TAB), to image amyloid structures with nanoscale resolution using amyloidophilic dyes, such as Thioflavin T, without the need for covalent labeling or immunostaining of the amyloid protein. Transient binding of ThT molecules to amyloid structures over time generates photon bursts that are used to localize single fluorophores with nanometer precision. …

Diagnostic Performance Of Receptor-Specific Surgical Specimen Staining Correlates With Receptor Expression Level, Jasmin M. Schaefer, Connor W. Barth, Scott C. Davis, Summer L. Gibbs

Dartmouth Scholarship

Intraoperative margin assessment is imperative to cancer cure but is a continued challenge to successful surgery. Breast conserving surgery is a relevant example, where a cosmetically improved outcome is gained over mastectomy, but re-excision is required in >25  %   of cases due to positive or closely involved margins. Clinical translation of margin assessment modalities that must directly contact the patient or required administered contrast agents are time consuming and costly to move from bench to bedside. Tumor resections provide a unique surgical opportunity to deploy margin assessment technologies including contrast agents on the resected tissues, substantially shortening the path to …

Ensuring Scientific Publishing Credibility In Translational Biomedical Optics., Brian W. Pogue

Dartmouth Scholarship

Optics has consistently been the largest singular technology sector used in medicine, and major advances in biomedical optics are documented daily in peer-reviewed publications. However, the academic stature of this field can be damaged by weaknesses in scientific publishing, where a “credibility crisis” has emerged as a popularized and increasingly studied dialogue. While there are still relatively few overt cases of fraud or erroneous research, more insidious aspects are seen in papers with results that have either low statistical power, selective reporting of observations, or data or computer codes that cannot be independently verified. Interestingly, the same solutions that improve …

Abso2luteu-Net: Tissue Oxygenation Calculation Using Photoacoustic Imaging And Convolutional Neural Networks, Kevin Hoffer-Hawlik, Geoffrey P. Luke

ENGS 88 Honors Thesis (AB Students)

Photoacoustic (PA) imaging uses incident light to generate ultrasound signals within tissues. Using PA imaging to accurately measure hemoglobin concentration and calculate oxygenation (sO₂) requires prior tissue knowledge and costly computational methods. However, this thesis shows that machine learning algorithms can accurately and quickly estimate sO₂. absO₂luteU-Net, a convolutional neural network, was trained on Monte Carlo simulated multispectral PA data and predicted sO₂ with higher accuracy compared to simple linear unmixing, suggesting machine learning can solve the fluence estimation problem. This project was funded by the Kaminsky Family Fund and the Neukom Institute.

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 …