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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Functional neuroimaging in survivors of torture

Ramasar, Thriyabhavan 17 January 2012 (has links)
Survivors of torture may have long-term physical, psychiatric and psychological sequelae. The aim of this study was to determine whether survivors of torture exhibit any psychopathology, whether they demonstrate abnormal findings on Brain Single Photon Emission Computed Tomography (SPECT) imaging, and whether correlations exist between Post Traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), perfusion changes on Brain SPECT and Initial Self Reporting Questionnaire (SRQ8) scores. Thirty-six volunteers were recruited in a non randomised manner. Participants were assessed by a psychiatrist. The SRQ8, Impact of Event Scale – Revised (IES-R) and Montgomery Asberg Depression Rating Scale (MADRS) were administered. Participants underwent Brain SPECT imaging to assess cerebral perfusion changes. Data was analysed using Statistica 9.1. The primary psychiatric diagnoses made were PTSD, MDD or both. Participants with psychopathology had higher SRQ8, MADRS and IES-R scores. Although qualitatively, participants with psychopathology showed increased abnormal cerebral perfusion on Brain SPECT imaging, as compared to those participants without psychopathology, this could not be proven statistically. Perfusion changes were noted in the temporal cortices, parietal cortices, orbitofrontal cortices, thalami and basal ganglia. Higher SRQ8 scores were associated with higher scores on the MADRS and IES-R, and hence correlated with diagnoses of MDD and PTSD, but no direct association was noted with the visualised abnormal Brain SPECT imaging findings.

Super resolution technique and its potential usage in medical imaging

Chang, Yiu-chuen, 張耀泉 January 2014 (has links)
Purpose: Medical imaging systems are used to scan patients to obtain valuable information for diseases diagnosis and assisting treatment. An ideal scanner should be sensitive enough to detect any trace amount of abnormal tissue at its early stage. With the continuous development of high-tech treatment systems such as Tomotherapy (manufactured by Tomo HD), the high-resolution imaging system is favorable to reduce the damage of normal tissue due to the image guidance of Mega-voltage beam before treatment. In this study, a software approach was presented to improve image resolution without hardware upgrade of a scanner. Methodology A programming technique “Super Resolution Technique” was used and demonstrated in an example of CT. It utilized several similar images with known relative shifts between them. (They can be positional or angular shifted and taken at the same time frame as far as possible). Those images are of low resolution and can be reconstructed to form a higher resolution image. A Super Resolution program was written by MATLAB to prove the method. The experiments 1 to 4 were purely computer-based simulations and experiment 5 used a LightSpeed VCT scanner for real scans. For the computer-based experiments, a few low resolution images have been attempted and registration steps were explored for image reconstruction. A resolution target, USAF1951, was called from MATLAB and used to examine the resolving power before and after image processing based on Super Resolution algorithm. Image-image subtraction was used to compare pre-processing and post-processing images. The number of non-zero pixels was used to access the percentage of similarity. For the experiment using LightSpeed VCT scanner, a GE VCT QA phantom was used to test the performance of the technique. Result From the experiments using USAF1951, it was found that: the minimum resolvable line pairs had improved from family -1 element 6 to family 0 element 2 (2 elements improvement) after applying “Super Resolution Technique” as shown in the experiment 1. An xy directional shifting of the pre-processing images resulted in a better reconstructed image than x-axis shifting or y-axis shifting in terms of resolution, shown in the experiment 2. The experiment 3 concluded that the more the pre-processing images, the better the reconstructed image would be. The experiment 4 showed that the shifts of pre-processing images greater than the detector size could still result in a higher resolution image. The experiment 5 revealed that applying “Super Resolution Technique” to a real CT scanner could not give an obvious improvement in resolution, but the image background noise had reduced. Conclusion It was concluded that the “Super Resolution Technique” could improve the image resolution and reduce the background noise at expense of more imaging time and more dose from the additional view. In case of hardware upgrade of imaging device is not practicable, Super Resolution could help improve the image quality. / published_or_final_version / Medical Sciences / Master / Master of Medical Sciences


MYERS, KYLE JEAN. January 1985 (has links)
This dissertation concerns the ability of human observers to perform detection tasks in medical images that contain structured noise. We shall show that physical measures of image quality, such as signal-to-noise ratio (SNR), resolution, modulation transfer function (MTF), and contrast, do not accurately predict how well an observer can detect lesions in an image. We have found that for images with equal pixel SNR, humans can detect a low contrast object more readily in images that have a low-pass noise structure, as opposed to a high-pass noise structure. This finding is important in the comparison of images generated by a classical pinhole imaging system with images generated by a computed tomography imager. We would like to have a figure of merit that accurately predicts a physician's ability to perform perceptual tasks. That is, we want a figure of merit for imaging systems that is more than an evaluation of the physician's performance, measured using human observers and an accepted method such as receiver operating characteristic (ROC) techniques. We want a figure of merit that we can calculate without requiring lengthy observer studies. To perform this calculation, we need a model of the imaging system hardware in cascade with a verified model of the human observer. We have chosen to approach this problem by modelling the human observer as an ideal observer. Our hypothesis is that the human observer acts approximately as an ideal-observer who does not have the ability to prewhiten the noise in an image. Without this ability, the ideal observer's detection performance for even a simple task is degraded substantially in correlated noise. This is just the effect that we have found for human observers. In search of a physiological explanation for a human observer's inability to do prewhitening, we shall investigate the detection capability of the ideal observer when a frequency-selective mechanism is invoked. This mechanism corresponds to the frequency channels known to exist in the human visual system. We shall show that the presence of such a mechanism can explain the degradation of human observer performance in correlated noise.

Parameterized computational imaging : optimized, data driven, and time-varying multiphysics modeling for image extension /

Evans, Daniel J. January 1900 (has links)
Thesis (Ph. D., Computer Science)--University of Idaho, August 2009. / Major professor: Mark L. Manwaring. Includes bibliographical references (leaves 172-179). Also available online (PDF file) by subscription or by purchasing the individual file.

Measurement of dose in diagnostic radiology and the effect of dose reduction on image quality

Egbe, Nneoyi Onen. January 2008 (has links)
Thesis (Ph.D.)--Aberdeen University, 2008. / Title from web page (viewed on June 3, 2009). Includes bibliographical references.

Image Processing as Applied to Medical Diagnostics

Thomas, Kristine A. 06 1900 (has links)
xi, 56 p. : ill. (some col.) A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / Image processing is a powerful tool for increasing the reliability and reproducibility of disease diagnostics. In the hands of pathologists, image processing provides quantitative data from histological images which supplement the qualitative data currently used by specialists. This thesis presents a novel method for analyzing digitized images of hematoxylin and eosin (H&E) stained histology slides to detect and quantify inflammatory polymorphonuclear leukocytes to aid in the grading of acute inflammation of the placenta as an example of the use of image processing in aid of diagnostics. Methods presented in this thesis include segmentation, a novel threshold selection technique and shape analysis. The most significant contribution is the automated color threshold selection algorithm for H&E stained histology slides which is the only unsupervised method published to date. / Committee in charge: Dr. John Conery, Chair; Dr. Matthew J. Sottile

Measurement of dose in diagnostic radiology and the effect of dose reduction on image quality

Egbe, Nneoyi Onen January 2008 (has links)
In computed radiography (CR) images, dose reduction up to 0.10 mGy is possible in chest radiography without image manipulation.  A lower dose of 0.06 mGy can be achieved when image manipulation is used for detection of lesions in the mediastinum.  Both clarity and detectability in the mediastinum improved by between 48 to 66% with image manipulation.  Abdominal images showed a significant difference at 2.69 mGy for the soft tissue area, suggesting caution in further dose reduction.  Image quality in the spinal area was improved significantly by 21 – 78.6% (for clarity) and 3 to 77% (for detectability) when image manipulation was employed. Comparatively, the image quality at the low doses studied was better for the film screen radiography than both processed and unprocessed CR images suggesting that low doses achieved in FSR may not be applicable to CR.  This difference may be attributed to the differences in the image receptors’ response to high photon energies, and the reduced number of x-ray quanta which produce lower subject contrast in FSR and reduced signal to noise ratio (SNR) as a result of increased noise in CR. Nigerian clay in its natural and salted forms cannot be used in radiation dosimetry in diagnostic radiology.  Paraffin wax/MgSO<sub>4</sub>.6H<sub>2</sub>0, and rice-gelatine (<i>rigel</i>) combinations as well as rice and gelatine used separately, have shown tissue equivalent x-ray attenuation at tube potentials above 80 kVp.  Paraffin wax/MgSO<sub>4</sub>.6H<sub>2</sub>0 and <i>rigel </i>can therefore be used as tissue substitutes.  Low patient entrance surface doses achieved in FSR may not produce equivalent image quality when applied to imaging with CR systems.  With respect to dose reduction, both modalities show the possibility of further dose reduction below current dose values by about 40% (chest) and 20% (abdomen), respectively, when used alone.

Interactive 3D GPU-Based Breast Mass Lesion Segmentation Method Based on Level Sets for Dce-MRI Images

Unknown Date (has links)
A new method for the segmentation of 3D breast lesions in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images, using parallel programming with general purpose computing on graphics processing units (GPGPUs), is proposed. The method has two main parts: a pre-processing step and a segmentation algorithm. In the pre-processing step, DCE-MRI images are registered using an intensity-based rigid transformation algorithm based on gradient descent. After the registration, voxels that correspond to breast lesions are enhanced using the Naïve Bayes machine learning classifier. This classifier is trained to identify four different classes inside breast images: lesion, normal tissue, chest and background. Training is performed by manually selecting 150 voxels for each of the four classes from images in which breast lesions have been confirmed by an expert in the field. Thirteen attributes obtained from the kinetic curves of the selected voxels are later used to train the classifier. Finally, the classifier is used to increase the intensity values of voxels labeled as lesions and to decrease the intensities of all other voxels. The post-processed images are used for volume segmentation of the breast lesions using a level set method based on the active contours without edges (ACWE) algorithm. The segmentation algorithm is implemented in OpenCL for the GPGPUs to accelerate the original model by parallelizing two main steps of the segmentation process: the computation of the signed distance function (SDF) and the evolution of the segmented curve. The proposed framework uses OpenGL to display the segmented volume in real time, allowing the physician to obtain immediate feedback on the current segmentation progress. The proposed implementation of the SDF is compared with an optimal implementation developed in Matlab and achieves speedups of 25 and 12 for 2D and 3D images, respectively. Moreover, the OpenCL implementation of the segmentation algorithm is compared with an optimal implementation of the narrow-band ACWE algorithm. Peak speedups of 55 and 40 are obtained for 2D and 3D images, respectively. The segmentation algorithm has been developed as open source software, with different versions for 2D and 3D images, and can be used in different areas of medical imaging as well as in areas within computer vision, such like tracking, robotics and navigation. / A Dissertation submitted to the Department of Scientific Computing in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Fall Semester 2015. / November 2, 2015. / GPU, Level Sets, OpenCL, OpenGL, Segmentation / Includes bibliographical references. / Anke Meyer-Baese, Professor Directing Dissertation; Mark Sussman, University Representative; Gordon Erlebacher, Committee Member; Dennis Slice, Committee Member; Xiaoqiang Wang, Committee Member.

Pattern Recognition in Medical Imaging: Supervised Learning of fMRI and MRI Data

Unknown Date (has links)
Machine learning algorithms along with magnetic resonance imaging (MRI) provides promising techniques to overcome the drawbacks of the current clinical screening techniques. In this study the resting-state functional magnetic resonance imaging (fMRI) to see the level of activity in a patient's brain and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to explore the level of improvement of neo-adjuvant chemotherapy in patients with locally advanced breast cancer were considered. As the first project, we considered fMRI of patients before and after they underwent a double-blind smoking cessation treatment. For the first time, this study aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction in nicotine-dependent patients and future treatment efficacy. In this regards, two classes of patients have been studied, one took the drug N-acetylcysteine and the other took a placebo. Our goal was to classify the patients as treatment or non-treatment, based on their fMRI scans. The image slices of brain are used as the variable. We have applied different voxel selection schemes and data reduction algorithms on all images. Then, we compared several multivariate classifiers and deep learning algorithms and also investigated how the different data reductions affect classification performance. For the second part, we have employed multi-parametric magnetic resonance imaging (mpMRI) using different morphological and functional MRI parameters such as T2-weighted, dynamic contrast-enhanced (DCE) MRI, and diffusion weighted imaging (DWI) has emerged as the method of choice for the early response assessments to NAC. Although, mpMRI is superior to conventional mammography for predicting treatment response, and evaluating residual disease, yet there is still room for improvement. In the past decade, the field of medical imaging analysis has grown exponentially, with an increased numbers of pattern recognition tools, and an increase in data sizes. These advances have heralded the field of radiomics. Radiomics allows the high-throughput extraction of the quantitative features that result in the conversion of images into mineable data, and the subsequent analysis of the data for an improved decision support with response monitoring during NAC being no exception. In this study. we determined the importance and ranking of the extracted parameters from mpMRI using T2-weighted, DCE, and DWI for prediction of pCR and patient outcomes with respect to metastases and disease-specific death employing different machine learning algorithms. / A Dissertation submitted to the Department of Scientific Computing in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester 2018. / July 6, 2018. / Breast Cancer, Data Mining, Machine Learning, Medical Imaging, Neuroimaging / Includes bibliographical references. / Anke Meyer-Baese, Professor Directing Dissertation; Simon Y. Foo, University Representative; Katja Pinker-Domenig, Committee Member; Peter Beerli, Committee Member; Dennis Slice, Committee Member.

Effective dose estimation for U.S. Army soldiers undergoing multiple computed tomography scans

Prins, Robert Dean January 2011 (has links)
Diagnosing the severity of blunt trauma injuries is difficult and involves the use of diagnostic radiological scanning. The primary diagnostic radiology modality used for assessing these injuries is computed tomography (CT). CT delivers more radiation dose than other diagnostic scanning modalities. Trauma patients are at an increased risk of radiation induced cancer because of the cumulative dose effects from multiple scanning procedures. Current methods for estimating effective dose, the quantity used to describe the whole body health detriment from radiation, involves the use of published conversion coefficients and procedure specific machine parameters such as dose-length-product based on computed tomography dose index and scan length. Other methods include the use of Monte Carlo simulations based upon the specific machine geometry and radiation source. Unless the requisite machine information is known, the only means of estimating the effective dose is through the use of generic estimates that are published by scientific radiation committees and have a wide range of values. This research addressed a knowledge gap in assigning effective doses from computed tomography when machine parameters knowledge is either unknown or incomplete. The research involved the development of a new method of estimating the effective dose from CT through the use of regression models incorporating the use of patient parameters as opposed to machine specific parameters. This new method was experimentally verified using two adult anthropomorphic phantoms and optically stimulated luminescent dosimeters. The new method was then compared against a real patient population undergoing similar computed tomography scanning procedures. Utilizing statistical procedures, the new method was tested for repeatability and bias against the current conversion coefficient method. The analysis of the new method verifies that the estimation ability is similar to recent research indicating that the older conversion coefficient methods can underestimate the effective dose to the patient by up to 40%. The new method can be used as a retrospective tool for effective dose estimation from CT trauma protocols for a patient population with physical characteristics similar to the U.S. Army Soldier population.

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