Radiology Commons^™

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. …

Fractal And Edge-Based Techniques For Kidney Enhancement And Segmentation On Magnetic Resonance Images (Mri), Alaá Rateb Mahmoud Al-Shamasneh

Student Works (2020-2029)

Recently, many rapid developments in digital medical imaging have made further contributions to healthcare systems. However, the segmentation of regions of interest in medical images plays a vital role in assisting doctors in their medical diagnoses and for the early detection of disease. Since health issues related to the kidneys are increasing exponentially, this thesis focused on developing methods for the segmentation of MRI images of the kidney. Kidney images frequently suffer from low contrast, low resolution and noise, and are blur. Hence, it is necessary to enhance the images in order to improve the segmentation. Therefore, the current thesis …

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 …

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 …

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 …

An Assessment Of Pet Dose Reduction With Penalized Likelihood Image Reconstruction Using A Computationally Efficient Model Observer, Howard C. Gifford, C. Ross Schmidtlein, Andrzej Krol, Yuesheng Xu

Mathematics & Statistics Faculty Publications

Developing PET reconstruction algorithms with improved low-count capabilities may provide a timely and cost- effective means of reducing radiation dose in promising clinical applications such as immuno-PET that require long-lived radiotracers. For many PET clinics, the reconstruction protocol consists of postsmoothed ordered-sets expectation-maximization (OSEM) reconstruction, but penalized likelihood methods based on total-variation (TV) regularization could substantially reduce dose. We performed a task-based comparison of postsmoothed OSEM and higher-order TV (HOTV) reconstructions using simulated images of a contrast-detail phantom. An anthropomorphic visual-search model observer read the images in a location-known receiver operating characteristic (ROC) format. Acquisition counts, target uptake, and target …

Deep Learning Based Medical Image Analysis With Limited Data, Jiaxing Tan

Dissertations, Theses, and Capstone Projects

Deep Learning Methods have shown its great effort in the area of Computer Vision. However, when solving the problems of medical imaging, deep learning’s power is confined by limited data available. We present a series of novel methodologies for solving medical imaging analysis problems with limited Computed tomography (CT) scans available. Our method, based on deep learning, with different strategies, including using Generative Adversar- ial Networks, two-stage training, infusing the expert knowledge, voting based or converting to other space, solves the data set limitation issue for the cur- rent medical imaging problems, specifically cancer detection and diagnosis, and shows very …

Sparsity Promoting Regularization For Effective Noise Suppression In Spect Image Reconstruction, Wei Zheng, Si Li, Andrzej Krol, C. Ross Schmidtlein, Xueying Zeng, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The purpose of this research is to develop an advanced reconstruction method for low-count, hence high-noise, Single-Photon Emission Computed Tomography (SPECT) image reconstruction. It consists of a novel reconstruction model to suppress noise while conducting reconstruction and an efficient algorithm to solve the model. A novel regularizer is introduced as the nonconvex denoising term based on the approximate sparsity of the image under a geometric tight frame transform domain. The deblurring term is based on the negative log-likelihood of the SPECT data model. To solve the resulting nonconvex optimization problem a Preconditioned Fixed-point Proximity Algorithm (PFPA) is introduced. We prove …

Compressed Sensing For Few-View Multi-Pinhole Spect With Applications To Preclinical Imaging, Benjamin Michael Rizzo

Dissertations (1934 -)

Single Photon Emission Computed Tomography (SPECT) can be used to identify and quantify changes in molecular and cellular targets involved in disease. A radiopharmaceutical that targets a specific metabolic function is administered to a subject and planar projections are formed by imaging emissions at different view angles around the subject. The reconstruction task is to determine the distribution of radioactivity within the subject from the projections. We present a reconstruction approach that utilizes only a few view angles, resulting in a highly underdetermined system, which could be used for dynamic imaging applications designed to quantify physiologic processes altered with disease. …

Estimating The Respiratory Lung Motion Model Using Tensor Decomposition On Displacement Vector Field, Kingston Kang

Theses and Dissertations

Modern big data often emerge as tensors. Standard statistical methods are inadequate to deal with datasets of large volume, high dimensionality, and complex structure. Therefore, it is important to develop algorithms such as low-rank tensor decomposition for data compression, dimensionality reduction, and approximation.

With the advancement in technology, high-dimensional images are becoming ubiquitous in the medical field. In lung radiation therapy, the respiratory motion of the lung introduces variabilities during treatment as the tumor inside the lung is moving, which brings challenges to the precise delivery of radiation to the tumor. Several approaches to quantifying this uncertainty propose using a …

A Study On Automated Process For Extracting White Blood Cellular Data From Microscopic Digital Injured Skeletal Muscle Images, Bibek Karki

UNLV Theses, Dissertations, Professional Papers, and Capstones

Skeletal muscle injury is one of the common injuries caused by high-intensity sports activities, military related works, and natural disasters. In order to discover better therapies, it is important to study muscle regeneration process. Muscle regeneration process tracking is the act of monitoring injured tissue section over time, noting white blood cell behavior and cell-fiber relations. A large number of microscopic images are taken for tracking muscle regeneration process over multiple time instances. Currently, manual approach is widely used to analyze a microscopic image of muscle cross section, which is time consuming, tedious and buggy.

Automation of this research methodology …

Image Enhancement Of Cancerous Tissue In Mammography Images, Richard Thomas Richardson

CCAC Theses and Dissertations

This research presents a framework for enhancing and analyzing time-sequenced mammographic images for detection of cancerous tissue, specifically designed to assist radiologists and physicians with the detection of breast cancer. By using computer aided diagnosis (CAD) systems as a tool to help in the detection of breast cancer in computed tomography (CT) mammography images, previous CT mammography images will enhance the interpretation of the next series of images. The first stage of this dissertation applies image subtraction to images from the same patient over time. Image types are defined as temporal subtraction, dual-energy subtraction, and Digital Database for Screening Mammography …

The Multimodal Brain Tumor Image Segmentation Benchmark (Brats), Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Khan M. Iftekharuddin, Syed M.S. Reza

Electrical & Computer Engineering Faculty Publications

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low-and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions …

Pharyngeal And Cervical Cancer Incidences Significantly Correlate With Personal Uv Doses Among Whites In The United States, Dianne E. Godar, Rong Tang, Stephen Merrill

Mathematics, Statistics and Computer Science Faculty Research and Publications

Because we found UV-exposed oral tissue cells have reduced DNA repair and apoptotic cell death compared with skin tissue cells, we asked if a correlation existed between personal UV dose and the incidences of oral and pharyngeal cancer in the United States. We analyzed the International Agency for Research on Cancer's incidence data for oral and pharyngeal cancers by race (white and black) and sex using each state's average annual personal UV dose. We refer to our data as ‘white’ rather than ‘Caucasian,’ which is a specific subgroup of whites, and ‘black’ rather than African-American because blacks from other countries …

3d Visualization Architecture For Building Applications Leveraging An Existing Validated Toolkit, David A. Polyak

Master's Theses (2009 -)

The diagnostic radiology space and healthcare in general is a slow adopter of new software technologies and patterns. Despite the widespread embrace of mobile technology in recent years, altering the manner in which societies in developed countries live and communicate, diagnostic radiology has not unanimously adopted mobile technology for remote diagnostic review. Desktop applications in the diagnostic radiology space commonly leverage a validated toolkit. Such toolkits not only simplify desktop application development but minimize the scope of application validation. For these reasons, such a toolkit is an important piece of a company’s software portfolio. This thesis investigated an approach for …

Skin Lesion Extraction And Its Application, Yanliang Gu

Dissertations, Master's Theses and Master's Reports - Open

In this thesis, I study skin lesion detection and its applications to skin cancer diagnosis. A skin lesion detection algorithm is proposed. The proposed algorithm is based color information and threshold. For the proposed algorithm, several color spaces are studied and the detection results are compared. Experimental results show that YUV color space can achieve the best performance. Besides, I develop a distance histogram based threshold selection method and the method is proven to be better than other adaptive threshold selection methods for color detection. Besides the detection algorithms, I also investigate GPU speed-up techniques for skin lesion extraction and …

Cervical Cancer Histology Image Feature Extraction And Classification, Peng Guo

Masters Theses

"Cervical cancer, the second most common cancer affecting women worldwide and the most common in developing countries can be cured if detected early and treated. Expert pathologists routinely visually examine histology slides for cervix tissue abnormality assessment. In previous research, an automated, localized, fusion-based approach was investigated for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 62 digitized histology images obtained through the National Library of Medicine. In this research, CIN grade assessments from two pathologists are analyzed and are used to facilitate atypical cell concentration feature development …

A Nonrigid Registration Method For Correcting Brain Deformation Induced By Tumor Resection, Yixun Liu, Chengjun Yao, Fotis Drakopoulos, Jinsong Wu, Liangfu Zhou, Nikos Chrisochoides

Computer Science Faculty Publications

Purpose: This paper presents a nonrigid registration method to align preoperative MRI with intraoperative MRI to compensate for brain deformation during tumor resection. This method extends traditional point-based nonrigid registration in two aspects: (1) allow the input data to be incomplete and (2) simulate the underlying deformation with a heterogeneous biomechanical model.

Methods: The method formulates the registration as a three-variable (point correspondence, deformation field, and resection region) functional minimization problem, in which point correspondence is represented by a fuzzy assign matrix; Deformation field is represented by a piecewise linear function regularized by the strain energy of a heterogeneous biomechanical …

Impact Of Varied Low Resolution Phantoms On Intensity Modulated Proton Therapy Dose Distributions, Aarohi Shyam Padhye

Theses Digitization Project

The primary purpose of this thesis is to discuss the usefulness of image segmentation techniques in creating accurate proton dose distribution plans. The calculation of the proton dose distribution has to take into account the material (tissue, bone, brain) in the treatment area of the patients body.

Stereotactic Localization And Targeting Accuracy For Experimental Proton Radiosurgery, Yin Chen

Theses Digitization Project

The purpose of this study was to improve an existing experimental proton radiosurgery system at Loma Linda University Medical Center to reach sub-millimeter accuracy before proton radiosurgery with narrow beams can be used in a clinical trial. Protons, different from photons (i.e., x-rays or gamma rays), are charged with particles that slow down in matter and release a burst of energy near the end of their range (maximum depth of penetration), which is called the Bragg peak, named after the physicist William Henry Bragg who discovered it in 1903. Photon beams deliver most doses over a large area near the …

Fiducial-Free Alignment Verification Techniques For Intracranial Radiosurgery, Kenneth Matthew Williams

Theses Digitization Project

This thesis serves as the basis for a method using image registration to automate patient alignment in an effort to eliminate the dependency on the fiducial markers as well as improve the accuracy efficiency of the alignment process. Proton beams are an external beam modality of radiation therapy that can be used effectively for radiosurgical applications due to the dosimetry advantage of the Bragg peak. The Bragg peak is a phenomenon exploited by proton beam therapy to concentrate the effect of the beams on the tumor while minimizing damage to critical structures and other health tissues within the patient.

A New Phantom And Gradient Isocenter Estimation For Magnetic Resonance Imaging Distortion Correction, Zongqi Cai

Theses Digitization Project

The purpose of this study was to develop and implement a numerical software based method that can accurately correct the distortion of MR images generated by 3T MRI scanner. To accomplish this, a new phantom has been designed from scratch to capture the distortions inside 3T MRI scanner. An algorithm has been developed, based on the unique geometric feature of the new phantom, to estimate the location of gradient isocenter of the magnetic field inside 3T MRI scanner for the first time.

Alternative Hull Detection Techniques For Preprocessing In Proton Computed Tomography Reconstruction, Blake Edward Schultze

Theses Digitization Project

The purpose of this study was to develop computationally efficient hull detection techniques appropriate for image reconstruction using sparse matrices. The hull detection techniques investigated were space carving (SC), modified space carving (MSC), and space modeling (SM) and these were compared to the cone-beam version of filtered back projection (FBP) algorithm in terms of their computation time and the quality of the object hull they produced.

Modular Machine Learning Methods For Computer-Aided Diagnosis Of Breast Cancer, Mia Kathleen Markey '94

Doctoral Dissertations

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models' predictions.

Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a …

The Next Step, Benjamin Y. Dai, Lynn Thompson, John David N. Dionisio, Hooshang Kangarloo, Ricky K. Taira

Computer Science Faculty Works

In traditional radiology practice, reports are typically dictated and then transcribed.? While the free-text reports represent the semantic knowledge interpreted and conveyed by a physician, the information can be hard to access. The advantages of representing medical data in a structured format using standard terminology are clearly recognized. These include the ability to implement a standardized electronic medical record, automatically invoke medical guidelines when appropriate, and conduct outcomes research. Standard structured reports facilitate intelligent indexing, searching, and retrieval of documents from clinical databases. Recent attempts have been made in the industry to enable structured data entry using preformatted templates, but …

Teleradiology As A Foundation For An Enterprise-Wide Health Care Delivery System, John David N. Dionisio, Ricky K. Taira, Usha Sinha, David B. Johnson, Benjamin Y. Dai, Gregory H. Tashima, Stephen Blythe, Richard Johnson, Hooshang Kangarloo

Computer Science Faculty Works

An effective, integrated telemedicine system has been developed that allows (a) teleconsultation between local primary health care providers (primary care physicians and general radiologists) and remote imaging subspecialists and (b) active patient participation related to his or her medical condition and patient education. The initial stage of system development was a traditional teleradiology consultation service between general radiologists and specialists; this established system was expanded to include primary care physicians and patients. The system was developed by using a well-defined process model, resulting in three integrated modules: a patient module, a primary health care provider module, and a specialist module. …

Integrated Multimedia Timeline Of Medical Images And Data For Thoracic Oncology Patients, Denise R. Aberle, John David N. Dionisio, Michael F. Mcnitt-Gray, Ricky K. Taira, Alfonso F. Cárdenas, Jonathan G. Goldin, Kathleen Brown, Robert A. Figlin, Wesley W. Chu

Computer Science Faculty Works

A prototype multimedia medical database has been developed to provide image and textual data for thoracic oncology patients undergoing treatment of advanced malignancies. The database integrates image data from the hospital pieture archiving and communication system with textual reports from the radiology information system, alphanumeric data contained in the hospital information system, and other electronic medical data. The database presents information in a timeline format and also contains visualization programs that permit the user to view and annotate radiographic measurements in tabular or graphic form. The database provides an efficient and intuitive display of the changing status of oncology patients. …

Development Of A Mammographic Image Processing Environment Using Matlab, John L. Kelley

Theses and Dissertations

Breast cancer is a disease that accounts for a disturbingly large number of deaths in females each year. Its prevalence is a topic of concern to all of us since it can affect our families, friends, and coworkers. Although mammographic screening is the most effective method currently available for the early detection of breast cancer, it is far from being an infallible procedure. Mammographic reading is error prone, partly because of the complexity of the task and partly because of the variability in human performance. Computers offer high reproducibility, and when used as an adjunct by the radiologist, may improve …

Quick Ceph™, A Computer Program For Cephalometric Analysis And Treatment Planning, Günther Blaseio

Loma Linda University Electronic Theses, Dissertations & Projects

The quantification of cephalometric radiographs is an integral part of orthodontic treatment planning. Numerous computer programs have been designed to digitize headfilms and to provide accurate graphic output and exact measurements. Yet these early systems were either huge, bulky and expensive or were limited in their practical use. The aim of this development was to combine modern computer and electronic equipment with advanced software engineering. The result is a universal cephalometric program written in the computer language "C" with presently seven standard and virtually limitless user definable analyses, growth forecasting, mouse-driven interactive VTO, arch length calculation, superimpositions, table of values …

Cephalometrics For The Oral Surgeon In The Diagnosis Of Facial Deformities, Frederick J. Mantz

Loma Linda University Electronic Theses, Dissertations & Projects

Surgical orthodontics presents a challenge to the oral surgeon. By the nature of the oral surgeon's training and discipline he should be the one best qualified to treat the surgical aspect of the surgical orthodontic case. The establishment of a proper diagnosis and means of communication between the oral surgeon and orthodontist are essential in achieving a good final result for the surgically treated case. Fortunately, a scientific method of diagnosis is available that can provide both specialties with a common language. Computerized cephalometric analysis can provide the diagnosis and link of communication for the discussion of goals and treatment. …