Browsing by Subject "Medical imaging"
Now showing items 1-20 of 175
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3D dynamic in vivo imaging of joint motion: application to measurement of anterior cruciate ligament function
(2019)More than 400,000 anterior cruciate ligament (ACL) injuries occur annually in the United States, 70% of which are non-contact. A severe consequence of ACL injury is the increased risk of early-onset of osteoarthritis (OA). ... -
A 3-D Multiparametric Ultrasound Elasticity Imaging System for Targeted Prostate Biopsy Guidance
(2023)Prostate cancer is the most common cancer and second-leading cause of cancer death among men in the United States. Early and accurate diagnosis of prostate cancer remains challenging; following an abnormal rectal exam or ... -
A Comparative Study of Radiomics and Deep-Learning Approaches for Predicting Surgery Outcomes in Early-Stage Non-Small Cell Lung Cancer (NSCLC)
(2022)Purpose: To compare radiomics and deep-learning (DL) methods for predicting NSCLC surgical treatment failure. Methods: A cohort of 83 patients undergoing lobectomy or wedge resection for early-stage NSCLC from our institution ... -
A Comprehensive Framework for Adaptive Optics Scanning Light Ophthalmoscope Image Analysis
(2019)Diagnosis, prognosis, and treatment of many ocular and neurodegenerative diseases, including achromatopsia (ACHM), require the visualization of microscopic structures in the eye. The development of adaptive optics ophthalmic ... -
A Prospective Method for Selecting the Optimal SPECT Pinhole Trajectory
(2021)AbstractPinhole imaging is a widely used method for high spatial resolution single gamma imaging with a small required field of view (FOV). Many factors affect pinhole imaging: (I) the geometric parameters of the pinhole ... -
A Radiomics Machine Learning Model for Post-Radiotherapy Overall Survival Prediction of Non-Small Cell Lung Cancer (NSCLC)
(2023)Purpose: To predict post-radiotherapy overall survival group of NSCLC patients based on clinical information and radiomics analysis of simulation CT. Materials/Methods: A total of 258 non-adenocarcinoma patients who received ... -
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation
(2023)AbstractPurpose: To develop a deep ensemble learning model with a radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric MRI (mp-MRI). Materials/Methods: This radiomics-incorporated ... -
ABSOLUTE QUANTIFICATION IN SMALL PLANT RADIOTRACER STUDIES
(2017)The main objective of this dissertation research is to develop measurement and data-analysis tools for improving the quantitative accuracy of radiotracer studies of small plants, e.g., grasses in their early growth stages ... -
Accelerating Brain DTI and GYN MRI Studies Using Neural Network
(2021)There always exists a demand to accelerate the time-consuming MRI acquisition process. Many methods have been proposed to achieve this goal, including deep learning method which appears to be a robust tool compared ... -
Adaptive Data Representation and Analysis
(2018)This dissertation introduces and analyzes algorithms that aim to adaptively handle complex datasets arising in the real-world applications. It contains two major parts. The first part describes an adaptive model of 1-dimensional ... -
Advanced Deep Learning Methods for Brain Metastasis Post-SRS Outcome Management
(2023)Purpose: The purpose of this study is to develop and validate two deep learning (DL) models for the management of brain metastasis (BM) patients treated with stereotactic radiosurgery (SRS). The first model is a ... -
Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems
(2016)X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount ... -
An Exploration of the Feasibility of Combining Radiation Therapy with Psoralen Phototherapy
(2018)Radiation therapy (RT) has been a standard-of-care treatment for many localized cancers for decades. Despite being an effective treatment modality for many clinical presentations, the efficacy of RT against cancer can be ... -
An Investigation of Machine Learning Methods for Delta-radiomic Feature Analysis
(2018)Background: Radiomics is a process of converting medical images into high-dimensional quantitative features and the subsequent mining these features for providing decision support. It is conducted as a potential noninvasive, ... -
An Investigation of MR Sequences for Partial Volume Correction in PET Image Reconstruction
(2019)Brain Positron emission tomography (PET) has been widely employed for the clinic diagnosis of Alzheimer's disease (AD). Studies have shown that PET imaging is helpful in differentiating healthy elderly individuals, mild ... -
Analysis of Rare Events and Multi-Object Radiomics in Medical Imaging
(2023)Introduction: Medical imaging is essential in oncology for detecting, diagnosing, and treating cancer, and monitoring treatment effectiveness. Radiomics and machine learning are techniques that use computer algorithms to ... -
Analysis of X-Ray Diffraction Imaging for Thick Tissue Imaging Using a GPU-Accelerated Monte Carlo Code
(2023)Our group has shown X-ray diffraction imaging for thin samples, however, its applicability to thick samples for pathology diagnostics, small animal imaging, and potentially in-vivo applications has yet to be explored. Single ... -
Assessing the feasibility of using deformable registration for on-board multi-modality based target localization in radiation therapy
(2018)Purpose: Cone beam computed tomography (CBCT) is typically used for on-board target localization in radiation therapy. However, CBCT has poor soft tissue contrast, which makes it extremely challenging to localize tumors ... -
Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI
(2016)Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate ... -
Assessment of Variability in Liver Tumor Contrast in MRI for Radiation Therapy
(2017)Purpose: To investigate the inter-patient and inter-sequence variation in liver tumor contrast in MRI and the feasibility of improving the liver tumor contrast by using an in-house developed multi-source adaptive fusion ...
