Radiology Commons^™

Spatial Computing With The Apple Vision Pro In Minimally Invasive Procedure Simulation: A Randomized Crossover Feasibility Study, Sydney Cooper, Aaron Kyle Jones, Rahul Anil Sheth, Koustav Pal, Bruno Odisio, Mark Blaylock, Shelita Kimble, Justin Bird, David Rice, Daniel Shoenthal, Emil Patel, Vipin Kamath, Sanjay Gupta, Jeffrey Siewerdsen, Joshua Kuban

Advances in Cancer Education and Quality Improvement

Purpose: This study aimed to evaluate the feasibility of wearing the Apple Vision Pro (AVP), a mixed-reality headset that integrates augmented and virtual reality, while performing minimally invasive procedures. While studies have demonstrated that spatial computing technology can improve surgical precision and reduce the risks of surgical complications, to our knowledge, no studies have specifically addressed the impact of the AVP on task performance during simulated image-guided procedures.

Materials and Methods: Thirteen diagnostic and interventional radiology residents performed image-guided central venous catheter placement, thoracentesis, and paracentesis on simulation models. Each participant completed a non-timed practice followed by the procedures once …

Dynamic Modeling Of The Earth's Trapped Proton Environment, Xiaojing Xu, Steve R. Blattnig, Francis F. Badavi, Martha S. Clowdsley, Edward J. Semones

Physics Faculty Publications

Context: Reliable prediction of space radiation exposure is critical for safeguarding spacecraft systems and ensuring astronaut health during missions. Accurate radiation risk assessment for space mission requires advanced models of the Earth’s trapped proton environment. These models must reflect temporal variations driven by geomagnetic field evolution and solar cycle modulation. Existing static models, such as AP8 and IRENE-AP9, are not designed to fully capture these evolving conditions. Aims: This paper presents a dynamic modeling method for the prediction of trapped proton fluxes, which incorporate time-dependent variations due to geomagnetic field evolution and solar cycle fluctuations. Methods: The …

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. …

Towards The Performance Characterization Of A Robotic Multimodal Diagnostic Imaging System, George Papaioannou, Christos Mitrogiannis, Mark Schweitzer, Nikolaos Michailidis, Maria Pappa, Pegah Khosravi, Apostolos Karantanas, Sean Starling, Christian Ruberg

Publications and Research

Characterizing imaging performance requires a multidisciplinary approach that evaluates various interconnected parameters, including dosage optimization and dynamic accuracy. Radiation dose and dynamic accuracy are challenged by patient motion that results in poor image quality. These challenges are more prevalent in the brain/cardiac pediatric patient imaging, as they relate to excess radiation dose that may be associated with various complications. Scanning vulnerable pediatric patients ought to eliminate anesthesia due to critical risks associated in some cases with intracranial hemorrhages, brain strokes, and congenital heart disease. Some pediatric imaging, however, requires prolonged scanning under anesthesia. It can often be a laborious, suboptimal …

Automated Segmentation Of The Ulnar Nerve In Mri Using Deep Learning Techniques, Akhil Nagulapalli

Theses

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting …

Novel Engineering Approaches For Orthopedic Injury Treatment And Infection Diagnosis, Cynthia Co

Bioengineering Dissertations - Archive

Bioengineering has made significant contributions to the treatment of orthopedic injury through interdisciplinary approaches. Central to these advancements are many orthopedic implants, including anchors and joint replacements, which have been designed to restore mobility. Despite their functional efficacy, these products have limited lifespan and are associated with many complications, such as implant-associated infection. The success of implant design and function, both in the short and long term, is paramount for enhancing patient outcomes and overall well-being. Therefore, my thesis work is dedicated to addressing these critical challenges, aiming to maximize their treatment effect while minimizing the implant-associated complications.

Long-term patient …

Different Spectrum Of Space Radiation Induced Cognitive Impairments In Radiation-Naïve And Adapted Rats, Richard A. Britten, Arriyam S. Fesshaye, Alyssa Tidmore, Ella N. Tamgue, Paola A. Alvarado-Arriaga

Center for Integrative Neuroscience and Inflammatory Diseases (CINID) Faculty Publications

NASA's decision to resume manned deep space mission, first to the Moon and then Mars, necessitated a detailed assessment of the potential health effects that astronauts may experience on long-duration missions. Multiple studies suggest that there may be significant space radiation (SR)-induced impairment of neurocognitive processes, including advanced executive functions. However, given the multitude of SR-induced changes in the CNS, it is possible that completely different SR-induced sequelae will be induced in previously exposed individuals. Thus, current risk estimates are likely to be pertinent only for the early stages of a deep space mission, and even then only for astronauts …

In Vivo Measurement Of Nadh Fluorescence Lifetime In Skeletal Muscle Via Fiber-Coupled Time-Correlated Single Photon Counting, Kathryn M. Priest, Jacob V. Schluns, Nathania Nischal, Colton L. Gattis, Jeffery C. Wolchok, Timothy J. Muldoon

Biomedical Engineering Faculty Publications and Presentations

Nicotinamide adenine dinucleotide (NADH) is a cofactor that serves to shuttle electrons during metabolic processes such as glycolysis, the tricarboxylic acid cycle, and oxidative phosphorylation (OXPHOS). NADH is autofluorescent, and its fluorescence lifetime can be used to infer metabolic dynamics in living cells. Fiber-coupled time-correlated single photon counting (TCSPC) equipped with an implantable needle probe can be used to measure NADH lifetime in vivo, enabling investigation of changing metabolic demand during muscle contraction or tissue regeneration. This study illustrates a proof of concept for point-based, minimally-invasive NADH fluorescence lifetime measurement in vivo. Volumetric muscle loss (VML) injuries were …

Auto-Segmentation In Pancreatic And Liver Radiation Therapy, Cenji Yu

Dissertations and Theses (Open Access)

Background

Gastrointestinal cancers exhibit a high mortality rate compared to other cancer types. Among these, pancreatic cancer ranks as the fourth leading cause of cancer-related deaths worldwide. The five-year survival rate remains alarmingly low at a mere 9%. Hepatocellular carcinoma (HCC), another aggressive form of cancer, is rapidly becoming the primary cause of cancer-related deaths in the United States. The treatment of both liver cancer and pancreatic cancer heavily relies on a multidisciplinary approach. Innovative treatment strategies involving dose-escalated regimens, such as stereotactic body radiation therapy (SBRT), are emerging as an important pillar of the management of liver and pancreatic …

Synthesis, Radiolabeling And Evaluation Of A Suite Of Tracers With 44Sc For Detecting Extracellular Dna, Zhiyao Li

McKelvey School of Engineering Graduate Student Theses & Dissertations

Neutrophil extracellular traps involve the rapid translocation of DNA to the outside of the cell under certain stimuli. This structure forms a fibrous network that is able to limit the spread of pathogens and to kill microorganisms. It has also been shown to be present in various pathological processes such as inflammation, autoimmune diseases, and cancer metastasis. Currently, the formation process of NETs in vivo is being extensively studied. However noninvasive detection and quantitation has yet to be achieved. A class of PET tracers are described here that consists of a DNA dye as the backbone that is labeled with …

Differentiating Axonal From Demyelinating Neuropathies Using Multiparametric Quantitative Mri Of Peripheral Nerves, Jacob D. Baraz, Stephanie Xuan, Sadaf Saba, Xue Yang, Ryan Castoro, Yang Xuan, Alison Roth, Richard D. Dortch, Jun Li, Yongsheng Chen

Medical Student Research Symposium

Objectives: To develop a multiparametric quantitative MRI (qMRI) method to track pathological changes in the peripheral neuropathies.

Background: Irrespective of the causes or types of polyneuropathies, peripheral nerves are mainly afflicted by two kinds of pathologies – axonal loss and demyelination. It is critical to differentiate between the two as treatments are different for the two conditions. While nerve conduction studies (NCS) have been used to differentiate the two pathologies in the distal nerves, there are no tools to probe the pathologies in the proximal peripheral nerves. This is particularly needed when distal nerves become non-responsive in NCS.

Methods: We …

Verification Of Proton Range Predictions In Proton Treatment Planning Using X-Ray Ct Or Proton Ct Imaging, Nicholas S. Yee

Graduate Research Theses & Dissertations

The objective of this paper is to compare the range errors for two treatment plans; one using x-ray CT and the other using proton CT for calculating dose distributions in an animal tissue phantom. We shot 150 MeV proton beam through a meat phantom to a film stack to measure the exponential drop of dose at the Bragg peak until zero dose remained. Plotting the depth dose curves and performing gamma analyses are two ways to quantify the proton range errors when comparing them to depth dose curves from film measurements in the phantom. The depth dose curves give us …

Machine Learning And Scalable Informatics Methods To Predict Disease Status From Multimodal Biomedical Data, Hossein Mohammadian Foroushani

McKelvey School of Engineering Graduate Student Theses & Dissertations

Biological understanding of complex diseases such as stroke and obesity is critical for the advancement of medicine. Further knowledge discovery can provide effective biomarkers to improve disease diagnosis and prognosis, identify driver mutations, predict individual genetic susceptibility for early prevention and effective disease management, and facilitate development of personalized drugs. Stroke is the second leading cause of death and long-term disability in the world. Thus, stroke management is a time-sensitive emergency. The initial hours after stroke onset map the trajectory of subsequent neurologic complications. Cerebral edema develops hours to days after acute ischemic stroke and may result in midline shift …

Evaluation And Clinical Implementation Of A Dual-Energy Ct Stopping-Power Ratio Mapping Technique For Proton-Therapy Treatment Planning, Maria Jose Medrano Matamoros

McKelvey School of Engineering Graduate Student Theses & Dissertations

Proton radiotherapy has the potential to treat tumors with better conformal dose distribution than competing modalities when the rapid dose falloff at the end of the proton-beam range is correctly aligned to the edge of the clinical target volume (CTV). However, its clinical potential is dependent on the accurate localization of the Bragg-peak position from predicted stopping-power ratio maps. The method that is most commonly used in today’s clinical practice for predicting stopping-power ratio (SPR) consists of a stoichiometric calibrationtechnique based on single-energy CT (SECT) for direct estimation of patient-specific SPR distribution from vendor-reconstructed Hounsfield Unit (HU) images. Unfortunately, this …

Advancing The Portability, Field Of View, And Spatiotemporal Resolution Of Multi-Parametric Photoacoustic Microscopy, Fenghe Zhong

McKelvey School of Engineering Graduate Student Theses & Dissertations

Capable of structural, functional, molecular, and metabolic imaging with high spatial resolution in vivo, photoacoustic microscopy (PAM) is an emerging tool in biomedical research. Further, recent advances in multi-parametric acquisition and analysis make PAM uniquely capable of simultaneously mapping the total concentration of hemoglobin (CHb), oxygen saturation of hemoglobin (sO2), and blood flow speed. However, there are multiple major limitations, including, the large footprint, low imaging speed, limited imaging contrast, and insufficient axial resolution, which impedance its applications in basic and translational research. In this dissertation, I presented potential solutions to overcome the limitations above.In the first part of this …

Radiomic Texture Feature Descriptor To Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal Mri, M. S. Sadique, A. Temtam, E. Lappinen, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can …

Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework …

Modeling Of Patient-Specific Periaortic Mechanics And Pulmonary Artery Hemodynamics Based On Phase-Contrast Magnetic Resonance Imaging Sequences., Johane H. Bracamonte

Theses and Dissertations

Inverse modeling in cardiovascular medicine is a collection of methodologies that can provide non-invasive patient-specific estimations of clinical risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs.

Herein, three different phase contrast magnetic resonance imaging (MRI) modalities were implemented as input data, displacement encoding with stimulated echoes (DENSE MRI) applied, and time-resolved velocity encoding phase-contrast MRI, in 1D and 3D, applied to pulmonary artery (PA) hemodynamics.

A model to account for the effect of periaortic interactions due to static and dynamic structures …

Multi-Vendor And Multisite Evaluation Of Cerebrovascular Reactivity Mapping Using Hypercapnia Challenge, Peiying Liu, Dengrong Jiang, Marilyn Albert, Christopher E. Bauer, Arvind Caprihan, Brian T. Gold, Steven M. Greenberg, Karl G. Helmer, Kay Jann, Gregory A. Jicha, Pavel Rodriguez, Claudia L Satizabal, Sudha Seshadri, Herpreet Singh, Jeffrey F. Thompson, Danny J. J. Wang, Hanzhang Lu

Neuroscience Faculty Publications

Cerebrovascular reactivity (CVR), which measures the ability of cerebral blood vessels to dilate or constrict in response to vasoactive stimuli such as CO2 inhalation, is an important index of the brain's vascular health. Quantification of CVR using BOLD MRI with hypercapnia challenge has shown great promises in research and clinical studies. However, in order for it to be used as a potential imaging biomarker in large-scale and multi-site studies, the reliability of CO2-CVR quantification across different MRI acquisition platforms and researchers/raters must be examined. The goal of this report from the MarkVCID small vessel disease biomarkers consortium is to evaluate …

Cessation And Resumption Of Elective Neurointerventional Procedures During The Coronavirus Disease 2019 Pandemic And Future Pandemics, Tim W. Malisch, Sameer A. Ansari, Gary R. Duckwiler, Kyle M. Fargen, Steven W. Hetts, Franklin A. Marden, Athos Patsalides, Clemens M. Schirmer, Allan Brook, Justin F. Fraser

Neurosurgery Faculty Publications

At the time of this writing, the coronavirus disease 2019 pandemic continues to be a global threat, disrupting usual processes, and protocols for delivering health care around the globe. There have been significant regional and national differences in the scope and timing of these disruptions. Many hospitals were forced to temporarily halt elective neurointerventional procedures with the first wave of the pandemic in the spring of 2020, in order to prioritize allocation of resources for acutely ill patients and also to minimize coronavirus disease 2019 transmission risks to non-acute patients, their families, and health care workers. This temporary moratorium on …

The State Of Artificial Intelligence In Medical Imaging, Catalin Cristian Veghes

School of Professional Studies

This study explores the current state of Artificial Intelligence in medical imaging and provides an accessible assessment of how radiologists perceive the emerging technologies. Throughout the research, we analyze different aspects such as the adoption rate of Artificial Intelligence or the performance of state-of-the-art models, and we identify some of the significant barriers that prevent a wider adoption, such as the lack of collaboration between radiologists and computer scientists. Additionally, we provide a brief theoretical background that explains how deep learning works and how it can be helpful in medical imaging. We describe the architecture of a binary classifier in …

Using Deep Learning To Analyze Materials In Medical Images, Carson Molder

Computer Science and Computer Engineering Undergraduate Honors Theses

Modern deep learning architectures have become increasingly popular in medicine, especially for analyzing medical images. In some medical applications, deep learning image analysis models have been more accurate at predicting medical conditions than experts. Deep learning has also been effective for material analysis on photographs. We aim to leverage deep learning to perform material analysis on medical images. Because material datasets for medicine are scarce, we first introduce a texture dataset generation algorithm that automatically samples desired textures from annotated or unannotated medical images. Second, we use a novel Siamese neural network called D-CNN to predict patch similarity and build …

Radiation‐Induced Oral Mucositis Hamster Model Using A Linear Accelerator Enhances Clinical Relevance Of Preclinical Studies For Treatment Strategy Investigation, Carolyn T. Jordan, Emily M. Bradford, Dennis C. Cheek, Mahesh R. Kudrimoti, Craig S. Miller, Molly H. Smith, J. Zach Hilt, Thomas D. Dziubla

Radiation Medicine Faculty Publications

Translational animal models for oral mucositis (OM) are necessary to simulate and assess the bioclinical effects and response in humans. These models should simulate high levels of radiation exposure that leads to oxidative stress and inflammatory‐initiated tissue changes. Hamster models have been extensively studied to observe pathological effects of radiation exposure and help in the development of effective treatments. To successfully evaluate the potential for treatment regimens with consistency and relevance, a radiation‐induced OM hamster model was developed using a clinical linear accelerator utilized by cancer patients daily. The dose exposure to the isolated, everted cheek pouch of a hamster, …

Focused Ultrasound-Mediated Drug Delivery To The Brainstem, Dezhuang Ye

McKelvey School of Engineering Graduate Student Theses & Dissertations

Brainstem gliomas are tumors that occur in the brainstem, the brain region that connects the brain to the spinal cord and controls vital body functions. The critical anatomic location of the brainstem precludes surgical intervention and limits the use of invasive therapeutic techniques. Moreover, the frequently intact blood-brain barrier (BBB) of most brainstem gliomas prevents therapeutic agents from reaching the diseased site. The currently available techniques for brain drug delivery are either invasive (e.g., convection-enhanced delivery) or lack targeting to the diseased site (e.g., intranasal brain drug delivery). Novel techniques that can noninvasively overcome the BBB are critically needed for …

Joint Modeling Of Rnaseq And Radiomics Data For Glioma Molecular Characterization And Prediction, Zeina A. Shboul, Norou Diawara, Arastoo Vossough, James Y. Chen, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations and Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Assessment Of Dobhoff Tube Malposition On Radiographs Using Deep Learning, Kevin George, Paras Lakhani, Md

Phase 1

Introduction: Dobhoff tubes (DHT) are narrow-bore flexible devices that deliver enteral nutrition for critically ill patients. Tracheobronchial insertion of DHTs presents a significant risk for pulmonary complications. Thus, DHT insertion requires radiologist confirmation of correct placement with chest x-ray (CXR), increasing clinical delays. To address this, we demonstrate the novel application of Deep Convolutional Neural Networks (DCNNs) to automatically and accurately identify DHTs in CXRs in real time.

Methods: 141 de-identified HIPAA compliant frontal view chest radiographs containing DHTs in various positions were obtained. The DHTs were first manually segmented and verified by a board certified radiologist. Images were split …

3d Convolutional Neural Networks For The Diagnosis Of 6 Unique Pathologies On Head Ct, Travis Clarke, Paras Lakhani, Md

Phase 1

Introduction: Head CT scans are a standard first-line tool used by physicians in the diagnosis of neurological pathologies. Recently, the development of deep learning models such as convolutional neural networks (CNNs) has allowed the rapid identification of bleeds and other pathologies on CT scans. This study aims to show that by training 3D CNNs with a larger, curated dataset, a more comprehensive list of potential diagnoses can be included in the detailed model.

Methods: A retrospective study was performed using a dataset of 66,000 head CT studies from the Thomas Jefferson University health system. Studies were acquired using a natural …

Automated Assessment Of Cardiothoracic Ratios On Chest Radiographs Using Deep Learning, Varun Danda, Paras Lakhani, Md

Phase 1

Introduction: The cardiothoracic ratio (CTR) is a quantitative measure of cardiac size that can measured from chest radiography (CXR). Although radiologists using digital workstations possess the ability to calculate CTR, clinical demands prevent calculation for every case. In this study, the efficacy of a deep convolutional neural network (dCNN) to assess CTR was evaluated.

Methods: 611 HIPAA-compliant de-identified CXRs were obtained from [institution blinded] and public databases. Using ImageJ, a board-certified radiologist (reader #1) and a medical student (reader #2), measured the CTR by marking four pixels on all CXRs: the right- and left-most chest wall, the right- and left-most …

Early Acid/Base And Electrolyte Changes In Permanent Middle Cerebral Artery Occlusion: Aged Male And Female Rats, Sarah R. Martha, Lisa A. Collier, Stephanie M. Davis, Sarah J. Goodwin, David Powell, Doug Lukins, Justin F. Fraser, Keith R. Pennypacker

Neurosurgery Faculty Publications

BACKGROUND: Early changes in acid/base and electrolyte concentrations could provide insights into the development of neuropathology at the onset of stroke. We evaluated associations between acid/base and electrolyte concentrations, and outcomes in permanent middle cerebral artery occlusion (pMCAO) model.

METHODS: 18-month-old male and female Sprague-Dawley rats underwent pMCAO. Pre-, post- (7 min after occlusion), and at 72 hr of pMCAO venous blood samples provided pH, carbon dioxide, oxygen, glucose, hematocrit, hemoglobin, and electrolyte values of ionized calcium, potassium, and sodium. Multiple linear regression determined predictors of infarct and edema volumes from these values, Kaplan-Meier curve analyzed morality between males and …