Bioimaging and Biomedical Optics Commons^™

Deep Learning In Lung Cancer Pre- And Post-Radiation Therapy: Diagnosis Of Malignancy And Radiation-Induced Lung Injury From 3d X-Ray Ct., Benjamin Peter Veasey

Electronic Theses and Dissertations

Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection and accurate diagnosis being critical for improving patient outcomes. Additionally, the progression of Radiation-Induced Lung Injury (RILI) following Stereotactic Body Radiation Therapy (SBRT) for lung cancer presents a significant diagnostic challenge. This dissertation addresses these challenges by developing deep learning-based diagnostic tools for both pre-treatment lung nodule malignancy classification and post-treatment RILI identification using 3D X-ray CT imaging. The research is divided into two primary objectives. First, for lung nodule malignancy classification, we developed a biopsy-confirmed dataset, called NLSTx, to train and evaluate deep learning models while …

Ai-Driven Approach For Diagnosis Of Renal Transplant Rejection Based On Biomarkers Identification And Integration., Israa Sharaby

Electronic Theses and Dissertations

They kidney is a vital organ for which humans are fortunate to find a spare through transplantation to sustain critical body functions, offering a hope to those struggling with renal failure. Kidney transplant procedure is the optimal treatment for people who suffer from end-stage renal failure. However, there are posed challenges due to the risk of immune rejection and the limited availability of donors. Early detection of renal rejection can provide timely intervention and accurate diagnosis that are critical to improve the transplant outcomes. This study explores the innovative approaches for addressing the current challenges through biomarkers identification, imaging techniques, …

Denoising And Super-Resolution Of In-Vitro 4e Flow Mri In A Stenotic Phantom Model Using Physics-Informed Neural Networks., Shrouk M. Wally

Electronic Theses and Dissertations

In recent years, the use of 4D flow MRI has revolutionized cardiovascular imag- ing by providing comprehensive data on blood flow dynamics over time. However, the limited spatial and temporal resolution of this imaging modality can hinder the accurate assessment of complex hemodynamic phenomena. This thesis explores the application of Physics-Informed Neural Networks (PINNs) to enhance the resolution of 4D flow MRI data, thereby improving its clinical utility. PINNs are a class of neural networks that integrate physical laws into their training process. By embedding these physics equations, PINNs can discover the underlying physics of fluid dynamics to produce more …

Cad System For Early Diagnosis Of Diabetic Retinopathy Based On 3d Extracted Imaging Markers., Ahmed Sleman

Electronic Theses and Dissertations

This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate …

The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah

Electronic Theses and Dissertations

Computational technologies can contribute to the modeling and simulation of the biological environments and activities towards achieving better interpretations, analysis, and understanding. With the emergence of digital pathology, we can observe an increasing demand for more innovative, effective, and efficient computational models. Under the umbrella of artificial intelligence, deep learning mimics the brain’s way in learn complex relationships through data and experiences. In the field of bioimage analysis, models usually comprise discriminative approaches such as classification and segmentation tasks. In this thesis, we study how we can use generative AI models to improve bioimage analysis tasks using Generative Adversarial Networks …

Machine Learning Approaches For Early Prediction Of Hypertension., Heba Elsayed Mohamed Kandil

Electronic Theses and Dissertations

Hypertension afflicts one in every three adults and is a leading cause of mortality in 516, 955 patients in USA. The chronic elevation of cerebral perfusion pressure (CPP) changes the cerebrovasculature of the brain and disrupts its vasoregulation mechanisms. Reported correlations between changes in smaller cerebrovascular vessels and hypertension may be used to diagnose hypertension in its early stages, 10-15 years before the appearance of symptoms such as cognitive impairment and memory loss. Specifically, recent studies hypothesized that changes in the cerebrovasculature and CPP precede the systemic elevation of blood pressure. Currently, sphygmomanometers are used to measure repeated brachial artery …

Imparting 3d Representations To Artificial Intelligence For A Full Assessment Of Pressure Injuries., Sofia Zahia

Electronic Theses and Dissertations

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning …

Material Properties Design Using Simulations And Experiments For Powder Injection Molding Of Lead Zirconate Titanate (Pzt)., Bhushan Pramod Bandiwadekar

Electronic Theses and Dissertations

Powder injection molding (PIM) process simulations can be performed to minimize the number of injection molding experiments by estimating material properties necessary for PIM simulations. In current work, lead zirconate titanate (PZT) powder-polymer binder feedstock was compounded for 45 vol. % and 52 vol. % solids loading. PIM experiments on designed micro-pillar array geometry were performed using 52 vol. % PZT. Using PIM experiments results as basis, PIM simulations were performed on designed micro-pillar array geometries to understand the effectiveness of PIM simulations with the use of estimated feedstock properties in predicting molding behavior that have micro-features. Additionally, PIM simulations …

A Novel Mra-Based Framework For The Detection Of Changes In Cerebrovascular Blood Pressure., Yitzhak Atakilt Gebru

Electronic Theses and Dissertations

Background: High blood pressure (HBP) affects 75 million adults and is the primary or contributing cause of mortality in 410,000 adults each year in the United States. Chronic HBP leads to cerebrovascular changes and is a significant contributor for strokes, dementia, and cognitive impairment. Non-invasive measurement of changes in cerebral vasculature and blood pressure (BP) may enable physicians to optimally treat HBP patients. This manuscript describes a method to non-invasively quantify changes in cerebral vasculature and BP using Magnetic Resonance Angiography (MRA) imaging.

Methods: MRA images and BP measurements were obtained from patients (n=15, M=8, F=7, Age= 49.2 …