Radiology Commons^™

Promises And Risks Of Applying Ai Medical Imaging To Early Detection Of Cancers, And Regulation For Ai Medical Imaging, Yiyao Zhang

The Journal of Purdue Undergraduate Research

No abstract provided.

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline For Oropharyngeal Cancer Radiotherapy Treatment Guidance, Kareem Wahid

Dissertations & Theses (Open Access)

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information …

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

McKelvey School of Engineering 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 …

Alignment And Range Verification In Proton Therapy Using Proton Radiography And Proton Ct, Joseph Piet

Graduate Research Theses & Dissertations

Protons are used in radiation therapy to lower doses to healthy tissues by utilizing their Bragg peak. Protons can be used both in imaging and treatment. One of the uses of protons in imaging we tested is its use to align patients using a single beam's eye proton radiograph (pRad). By using a beam's eye pRad, and comparing the water equivalent thickness (WET) to proton digitally reconstructed radiographs (pDRRs), we show that we can measure the best alignment on six axes, three translational and three rotational. This is done by defining a cost function, chi squared, which quantifies the misalignment …

Prediction Of Rapid Early Progression And Survival Risk With Pre-Radiation Mri In Who Grade 4 Glioma Patients, Walia Farzana, Mustafa M. Basree, Norou Diawara, Zeina Shboul, Sagel Dubey, Marie M. Lockheart, Mohamed Hamza, Joshua D. Palmer, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Rapid early progression (REP) has been defined as increased nodular enhancement at the border of the resection cavity, the appearance of new lesions outside the resection cavity, or increased enhancement of the residual disease after surgery and before radiation. Patients with REP have worse survival compared to patients without REP (non-REP). Therefore, a reliable method for differentiating REP from non-REP is hypothesized to assist in personlized treatment planning. A potential approach is to use the radiomics and fractal texture features extracted from brain tumors to characterize morphological and physiological properties. We propose a random sampling-based ensemble classification model. The proposed …

Comparison Of Physics-Based Deformable Registration Methods For Image-Guided Neurosurgery, Nikos Chrisochoides, Yixun Liu, Fotis Drakopoulos, Andriy Kot, Panos Foteinos, Christos Tsolakis, Emmanuel Billias, Olivier Clatz, Nicholas Ayache, Andrey Fedorov, Alex Golby, Peter Black, Ron Kikinis

Computer Science Faculty Publications

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete …

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 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 …

Absolute Quantification Of Tc-99m Activity Distributions Using A Planar Molecular Breast Imaging Commercial System, Benjamin P. Lopez

Dissertations & Theses (Open Access)

Molecular breast imaging (MBI) uses two dedicated-breast semiconductor detectors to visualize the preferential uptake of technetium-99m-sestamibi (^99mTc-sestamibi) by breast cancer cells relative to surrounding benign breast tissues. Clinically, MBI is used primarily as a supplementary tool to standard-of-care mammography because of its improved detection of breast cancers, especially in women with mammographically-dense breasts. Because of a lack of image corrections, MBI applications are currently limited to qualitative evaluations of relative pixel intensities between image regions with suspected lesions and normal tissue.

The objective of this dissertation was to use Monte Carlo simulations to better characterize the MBI imaging …

Hepatocellular Carcinoma Image-Guided Intervention: Quantitative Characterization Of Reagents For Thermochemical Ablation, Emily A. Thompson

Dissertations & Theses (Open Access)

Thermochemical ablation (TCA) is a minimally invasive therapy under development for hepatocellular carcinoma, a leading cause of cancer death worldwide. TCA utilizes acid-base chemistry delivered simultaneously to induce local ablation when administered. When delivered via a mixing catheter placed directly into the tumor, acid (e.g., AcOH) and base (e.g., NaOH) react to completion at the catheter tip, producing the acetate salt, water, and releasing heat (Δ>50°C) in sufficient quantities to induce lethal osmotic and thermal stress in tumor cells. However, these two reagents are not distinguishable from tissues with noninvasive imaging modalities, which makes monitoring the delivery of TCA …

Understanding Deep Learning - Challenges And Prospects, Niha Adnan, Fahad Umer

Department of Surgery

The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial Intelligence techniques are making serious progress in the diagnostic and treatment planning aspects of dental clinical practice. This will ultimately help in the elimination of subjectivity and human error that are often part of radiographic interpretations, and will improve the overall efficiency of the process. The various types of Artificial Intelligence algorithms that exist today make the understanding of their application quite complex. …

Blood Flow Restriction Training After Patellar Instability (Brains Trial), Benjamin D. Brightwell, Austin V. Stone, Xiaojuan Li, Peter A. Hardy, Katherine L. Thompson, Brian W. Noehren, Cale A. Jacobs

Orthopaedic Surgery and Sports Medicine Faculty Publications

Background

Patellar instability is a common and understudied condition that disproportionally affects athletes and military personnel. The rate of post-traumatic osteoarthritis that develops following a patellar dislocation can be up to 50% of individuals 5–15 years after injury. Conservative treatment is the standard of care for patellar instability however, there are no evidence-informed rehabilitation guidelines in the scientific literature. The purpose of this study is to assess the effectiveness of blood-flow restriction training (BFRT) for patellar instability. Our hypotheses are that this strategy will improve patient-reported outcomes and accelerate restoration of symmetric strength and knee biomechanics necessary to safely return …

System Measurements For X-Ray Phase And Diffraction Imaging, Erik Wolfgang Tripi

Legacy Theses & Dissertations (2009 - 2024)

In medical imaging, X rays are used to look inside the body to find fractures in bones, abnormal masses, cavities in teeth, and so on. What makes X rays so good at looking at these types of structures is the X ray’s penetration power. When imaging soft tissue to search for tumors, X-ray images tend to have difficulty performing well. The reason for this is that the background structures, such as fat or fibro glandular tissue have similar absorption coefficients as the tumor. Mammography tends to have a high false positive rate and can miss tumors entirely as well. There …

Statistical Measurements Of Dispersion Measure Fluctuations Of Frbs, Siyao Xu, David H. Weinberg, Bing Zhang

Physics & Astronomy Faculty Research

Extragalactic fast radio bursts (FRBs) have large dispersion measures (DMs) and are unique probes of intergalactic electron density fluctuations. By using the recently released First CHIME/FRB Catalog, we reexamined the structure function (SF) of DM fluctuations. It shows a large DM fluctuation similar to that previously reported in Xu & Zhang, but no clear correlation hinting toward large-scale turbulence is reproduced with this larger sample. To suppress the distortion effect from FRB distances and their host DMs, we focus on a subset of CHIME catalog with DM < 500 pc cm-3. A trend of nonconstant SF and nonzero correlation function (CF) at angular separations θ less than 10 is seen, but with large statistical uncertainties. The difference found between SF and that derived from CF at θ ≲ 10 can be ascribed to the large statistical uncertainties or the density inhomogeneities on scales on the order of 100 Mpc. The possible correlation of electron density fluctuations and inhomogeneities of density distribution should be tested when several thousands of FRBs are available.

Comparing Semi-Automated Segmentation Of Traditional-Resolution And High-Resolution Hyperpolarized 129xe Mri On Covid-19 Survivors, Tingting Wu

Undergraduate Student Research Internships Conference

Hyperpolarized gas MRI using inert gases like Xe is a valuable tool in visualizing lung ventilation in patients, and can be used as a longitudinal monitoring tool for patients with lung diseases. However, use of this method requires segmentation and quantification of parameters such as ventilation defect percentage (VDP), which is often very subjective depending on the observer. This study aimed to determine the accuracy and consistency of VDP calculation using the same MRI scans from COVID-19 patients, but with high resolution and low (traditional) resolution versions. Using a MATLAB script developed previously, it was found that in general, using …

From Mathematics To Medicine: A Practical Primer On Topological Data Analysis (Tda) And The Development Of Related Analytic Tools For The Functional Discovery Of Latent Structure In Fmri Data, Andrew Salch, Adam Regalski, Hassan Abdallah, Raviteja Suryadevara, Michael J. Catanzaro, Vaibhav A. Diwadkar

Mathematics Faculty Research Publications

fMRI is the preeminent method for collecting signals from the human brain in vivo, for using these signals in the service of functional discovery, and relating these discoveries to anatomical structure. Numerous computational and mathematical techniques have been deployed to extract information from the fMRI signal. Yet, the application of Topological Data Analyses (TDA) remain limited to certain sub-areas such as connectomics (that is, with summarized versions of fMRI data). While connectomics is a natural and important area of application of TDA, applications of TDA in the service of extracting structure from the (non-summarized) fMRI data itself are heretofore nonexistent. …

Quantitative Magnetic Resonance Imaging For The Early Prediction Of Treatment Response In Triple Negative Breast Cancer, Benjamin C. Musall

Dissertations & Theses (Open Access)

Triple Negative Breast Cancer (TNBC) is an aggressive subtype of breast cancer which lacks upregulated hormone receptors. Because of this, it is not vulnerable to clinically available targeted therapies. When treated with standard of care neoadjuvant systemic therapy (NAST), TNBC only shows approximately a 40% rate of pathologic complete response (pCR). A biomarker which could predict TNBC response to NAST early during treatment would be useful, as it would allow for non-responders to be triaged to alternative therapies and potentially allow for the treatment of responders to be de-escalated.

Quantitative Magnetic Resonance Imaging (MRI) may be used to probe and …

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 …

A Deep Learning U-Net For Detecting And Segmenting Liver Tumors, Vidhya Cardozo

Theses and Dissertations

Visualization of liver tumors on simulation CT scans is challenging even with contrast-enhancement, due to the sensitivity of the contrast enhancement to the timing of the CT acquisition. Image registration to magnetic resonance imaging (MRI) can be helpful for delineation, but differences in patient position, liver shape and volume, and the lack of anatomical landmarks between the two image sets makes the task difficult. This study develops a U-Net based neural network for automated liver and tumor segmentation for purposes of radiotherapy treatment planning. Non-contrast simulation based abdominal CT axial scans of 52 patients with primary liver tumors were utilized. …

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. …

Intense Monochromatic Photons Above 100 Kev From An Inverse Compton Source, Kirsten Deitrick, Georg H. Hoffstaetter, Carl Franck, Bruno D. Muratori, Peter H. Williams, Geoffrey A, Krafft, Balša Terzić, Joe Crone, Hywel Owen

Physics Faculty Publications

Quasimonochromatic x rays are difficult to produce above 100 keV, but have a number of uses in x-ray and nuclear science, particularly in the analysis of transuranic species. Inverse Compton scattering (ICS) is capable of fulfilling this need, producing photon beams with properties and energies well beyond the limits of typical synchrotron radiation facilities. We present the design and predicted output of such an ICS source at CBETA, a multiturn energy-recovery linac with a top energy of 150 MeV, which we anticipate producing x rays with energies above 400 keV and a collimated flux greater than 108 photons per second …

Therapy And Medical Imaging Applications Of Focusing Polycapillary X-Ray Optics, Weiyuan Sun

Legacy Theses & Dissertations (2009 - 2024)

Focusing polycapillary optics yield high gains in intensity and increased spatial resolution for a variety of clinical, lab-based, synchrotron, or in situ analysis applications. In this dissertation we investigate the extension of two applications of focusing polycapillary optics. The first is the application of polycapillary optics in radiation therapy. This discussion includes measurements and calculation of dose for focused beam orthovoltage therapy. A system has been designed to investigate whether the polycapillary optics can produce an X-ray beam which can give more accurate dose painting due to the higher dose concentration at the focal spot. X-ray exposures were measured with …

Hyperpolarized Carbon-13 Magnetic Resonance Measurements Of Tissue Perfusion And Metabolism, Keith Michel

Dissertations & Theses (Open Access)

Hyperpolarized Magnetic Resonance Imaging (HP MRI) is an emerging modality that enables non-invasive interrogation of cells and tissues with unprecedented biochemical detail. This technology provides rapid imaging measurements of the activity of a small quantity of molecules with a strongly polarized nuclear magnetic moment. This polarization is created in a polarizer separate from the imaging magnet, and decays continuously towards a non-detectable thermal equilibrium once the imaging agent is removed from the polarizer and administered by intravenous injection. Specialized imaging strategies are therefore needed to extract as much information as possible from the HP signal during its limited lifetime.

In …

Integrated Multiparametric Radiomics And Informatics System For Characterizing Breast Tumor Characteristics With The Oncotypedx Gene Assay, Michael A. Jacobs, Christopher B. Umbricht, Vishwa S. Parekh, Riham H. El Khouli, Leslie Cope, Katarzyna J. Macura, Susan Harvey, Antonio C. Wolff

Radiology Faculty Publications

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained …

Mapping Gadolinium Contrast In A Complex Ionic And Photosynthesis Environment Of Pineapple By Near-Infrared And X-Ray Imaging, Subhendra Sarkar, Zoya Vinokur, Chen Xu, Tetiana Soloviova, Amina Shahbaz, Aldona Gjoni

Publications and Research

This work explores the diffusivity of a lanthanide complex, Eovist (Gadolinium-Ethoxy Benzyl Diethylenetriamine pentaacetate) that is stable in neutral media but is not in acidic environment. In the current work an acidic fruit model like pineapple that is rich in transition metals was used and a possible transmetallation reaction among Eovist and transition metal complexes was tested using X-ray imaging. Another goal of this work was to perturb the usual and the unusual photosynthesis systems that pineapple has maintained for millions of years during the evolution of circadian genes for efficient water conservation by dark photosynthesis. To detect such photosynthesis …

Phantoms To Placentas: Mr Methods For Oxygen Quantification, Kelsey Meinerz

Arts & Sciences Electronic Theses and Dissertations

Molecular oxygen (O2) is vital for efficient energy production and improper oxygenation is a hallmark of disease or metabolic dysfunction. In many pathologies, knowledge of tissue oxygen levels (pO2) could aid in diagnosis and treatment planning. The gold standard for pO2 measures in tissue are implantable probes, which are invasive, require surgery for placement, and are inaccessible to certain regions of the body. Methods for determining pO2 both non-invasively and quantitatively are lacking. The slight paramagnetic nature of O2 provides opportunities to non-invasively characterize pO2 in tissue via magnetic resonance (MR) techniques. As such, O2 can be treated as a …

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

Dissertations & 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 …

Potential Impacts Of Artificial Intelligence On Spine Imaging Interpretation And Diagnosis, David Howard Durrant

Walden Dissertations and Doctoral Studies

Spine and related disorders represent one of the most common causes of pain and disability in the United States. Imaging represents an important diagnostic procedure in spine care. Imaging studies contain actionable data and insights undetectable through routine visual analysis. Convergent advances in imaging, artificial intelligence (AI), and radiomic methods has revealed the potential of multiscale in vivo interrogation to improve the assessment and monitoring of pathology. AI offers various types of decision support through the analysis of structured and unstructured data. The primary purpose of this qualitative exploratory case study was to identify the potential impacts of AI solutions …

Synthesis Of Radioluminescent Caf2:Ln Core, Mesoporous Silica Shell Nanoparticles For Use In X-Ray Based Theranostics, Hayden Winter, Megan J. Neufeld, Lydia Makotamo, Conroy Sun, Andrea M. Goforth

Chemistry Faculty Publications and Presentations

X-ray radiotherapy is a common method of treating cancerous tumors or other malignant lesions. The side effects of this treatment, however, can be deleterious to patient quality of life if critical tissues are affected. To potentially lower the effective doses of radiation and negative side-effects, new classes of nanoparticles are being developed to enhance reactive oxygen species production during irradiation. This report presents the synthesis and radiotherapeutic efficacy evaluation of a new nanoparticle formulation designed for this purpose, composed of a CaF₂ core, mesoporous silica shell, and polyethylene glycol coating. The construct was additionally doped with Tb and Eu …

Framework For Algorithmically Optimizing Longitudinal Health Outcomes: Examples In Cancer Radiotherapy And Occupational Radiation Protection, Lydia Joyce Wilson

LSU Doctoral Dissertations

Background: Advancements in the treatment of non-infectious disease have enabled survival rates to steadily increase in recent decades (e.g., diabetes, heart disease, and cancer). Epidemiological studies have revealed that the treatments for these diseases can have life-threatening and/or life–altering effects. Thus, realizing the full beneficial potential of advanced treatments necessitates new tools to algorithmically consider all major components of the health outcome, including benefit and detriment. The goal of this dissertation was to develop a framework for improving projected health outcomes following planned radiation exposures in consideration of all beneficial and detrimental, early and late, and fatal and non-fatal …

Deep Learning, Medical Physics And Cargo Cult Science., Miguel Romero Phd, Gilmer Valdes Phd, Timothy Solberg Phd, Yannet Interian Phd

Creative Activity and Research Day - CARD

Deep learning algorithms have become widely popular, with considerable success in fields where datasets have hundreds of thousands or million points. As deep learning is increasingly applied to the fields of medical physics and radiation oncology, a reasonable question follows: are these techniques the best approach, given the unique conditions in our field? In this study, we investigate the dependence of dataset size on the performance of deep learning algorithms compared with more traditional radiomics-based methods.