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REDUCING EDDY CURRENTS IN HIGH MAGNETIC FIELD ENVIRONMENTS
Title
REDUCING EDDY CURRENTS IN HIGH MAGNETIC FIELD ENVIRONMENTS.
Creator
Case, Russell, Weeks, Arthur, University of Central Florida
Abstract / Description
When an electrical conducting volume is placed into the bore of an MRI undergoing an image scan, time varying magnetic gradients induce eddy currents in this conducting material. These eddy currents in turn produce a mechanical torque on this volume. It is the goal of this thesis to produce a computer simulation of eddy currents produced by placing conducting materials inside an MRI bore. The first part of the thesis establishes the physics and principles behind an MRI system along with...
Show moreWhen an electrical conducting volume is placed into the bore of an MRI undergoing an image scan, time varying magnetic gradients induce eddy currents in this conducting material. These eddy currents in turn produce a mechanical torque on this volume. It is the goal of this thesis to produce a computer simulation of eddy currents produced by placing conducting materials inside an MRI bore. The first part of the thesis establishes the physics and principles behind an MRI system along with several applications. Next, this thesis presents an analysis of eddy current effects produced on a conductor placed into an MRI bore. The design and construction of simulated MRI magnetic fields is then presented along with a study of simulated eddy currents in various test conducting volumes of selected materials. Finally, techniques are discussed for reducing eddy currents in these conducting volumes and materials, along with simulation results showing the reduction in the applied eddy current. The findings of this thesis are summarized in the conclusions and recommendations are made for modification and future applications of these techniques and simulations.
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Date Issued
2008
Identifier
CFE0002015, ucf:47611
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002015
A STRUCTURAL AND FUNCTIONAL ANALYSIS OF HUMAN BRAIN MRI WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER
Title
A STRUCTURAL AND FUNCTIONAL ANALYSIS OF HUMAN BRAIN MRI WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER.
Creator
Watane, Arjun A, Bagci, Ulas, University of Central Florida
Abstract / Description
Attention Deficit Hyperactivity Disorder (ADHD) affects 5-10% of children worldwide. Its effects are mainly behavioral, manifesting in symptoms such as inattention, hyperactivity, and impulsivity. If not monitored and treated, ADHD may adversely affect a child's health, education, and social life. Furthermore, the neurological disorder is currently diagnosed through interviews and opinions of teachers, parents, and physicians. Because this is a subjective method of identifying ADHD, it is...
Show moreAttention Deficit Hyperactivity Disorder (ADHD) affects 5-10% of children worldwide. Its effects are mainly behavioral, manifesting in symptoms such as inattention, hyperactivity, and impulsivity. If not monitored and treated, ADHD may adversely affect a child's health, education, and social life. Furthermore, the neurological disorder is currently diagnosed through interviews and opinions of teachers, parents, and physicians. Because this is a subjective method of identifying ADHD, it is easily prone to error and misdiagnosis. Therefore, there is a clear need to develop an objective diagnostic method for ADHD. The focus of this study is to explore the use of machine language classifiers on information from the brain MRI and fMRI of both ADHD and non-ADHD subjects. The imaging data are preprocessed to remove any intra-subject and inter-subject variation. For both MRI and fMRI, similar preprocessing stages are performed, including normalization, skull stripping, realignment, smoothing, and co-registration. The next step is to extract features from the data. For MRI, anatomical features such as cortical thickness, surface area, volume, and intensity are obtained. For fMRI, region of interest (ROI) correlation coefficients between 116 cortical structures are determined. A large number of image features are collected, yet many of them may include redundant and useless information. Therefore, the features used for training and testing the classifiers are selected in two separate ways, feature ranking and stability selection, and their results are compared. Once the best features from MRI and fMRI are determined, the following classifiers are trained and tested through leave-one-out cross validation, experimenting with varying feature numbers, for each imaging modality and feature selection method: support vector machine, support vector regression, random forest, and elastic net. Thus, there are four experiments (MRI-rank, MRI-stability, fMRI-rank, fMRI-stability) with four classifiers in each for a total of 16 classifiers trained per each feature count attempted. The results of each classifier are the decisions of each subject, ADHD or non-ADHD. Finally, a classifier decision ensemble is created through the combination of the outputs of the best classifiers in a majority voting method that includes results of both the MRI and fMRI classifiers and keeps both feature selection results independent. The results suggest that ADHD is more easily identified through fMRI because the classification accuracies are a lot higher using fMRI data rather than MRI data. Furthermore, significant activity correlation differences exist between the brain's frontal lobe and cerebellum and also the left and right hemispheres among ADHD and non-ADHD subjects. When including MRI decisions with fMRI in the classifier ensemble, performance is boosted to a high ADHD detection accuracy of 96.2%, suggesting that MRI information assists in validating fMRI classification decisions. This study is an important step towards the development of an automatic and objective method for ADHD diagnosis. While more work is needed to externally validate and improve the classification accuracy, new applications of current methods with promising results are introduced here.
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Date Issued
2017
Identifier
CFH2000203, ucf:45978
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFH2000203
Design and Motion Control of a Four Degree of Freedom Robotic Needle Guide for MRI-Guided Intervention
Title
Design and Motion Control of a Four Degree of Freedom Robotic Needle Guide for MRI-Guided Intervention.
Creator
Zhang, Shihao, Song, Sang-Eun, Xu, Yunjun, Bagci, Ulas, University of Central Florida
Abstract / Description
In the past several MRI compatible robotic needle guide devices for targeted prostate biopsy have been developed. The large and complex structure have been identified as the major limitations of those devices. Such limitations, in addition to complex steps for device to image registration have prevented widespread implementation of MRI-guided prostate biopsy despite the advantages of MRI compared to TRUS. We have designed a compact MRI-guided robotic intervention with the capability to have...
Show moreIn the past several MRI compatible robotic needle guide devices for targeted prostate biopsy have been developed. The large and complex structure have been identified as the major limitations of those devices. Such limitations, in addition to complex steps for device to image registration have prevented widespread implementation of MRI-guided prostate biopsy despite the advantages of MRI compared to TRUS. We have designed a compact MRI-guided robotic intervention with the capability to have angulated insertion to avoid damage to any anatomical feature along the needle path. The system consists of a novel mechanism driven Robotic Needle Guide (RNG). The RNG is a 4-DOF robotic needle manipulator mounted on a Gross Positioning Module (GPM), which is locked on the MRI table. The RNG consists of four parallel stacked disks with an engraved profile path. The rotary motion and positioning of the discs at an angle aids in guiding the biopsy needle. Once a clinician selects a target for needle insertion, the intervention provides possible insertion angles. Then, the most suitable angle is selected by the clinician based on the safest trajectory. The selected target and insertion angle are then computed as control parameters of RNG i.e. the discs are then rotated to the required angle. Insertion is followed by quick confirmation scans to ascertain needle position at all times.
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Date Issued
2018
Identifier
CFE0007116, ucf:51942
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007116
A Short Window Granger Causality Approach to Identify Brain Functional Pattern Associated with Changes of Performance Induced by Sleep Deprivation
Title
A Short Window Granger Causality Approach to Identify Brain Functional Pattern Associated with Changes of Performance Induced by Sleep Deprivation.
Creator
Li, Muyuan, Karwowski, Waldemar, Xanthopoulos, Petros, Hancock, Peter, Mikusinski, Piotr, University of Central Florida
Abstract / Description
The comprehensive effect of sleep deprivation on biological and behavioral functions largely remains unknown. There is evidence to support that human sleep must be of sufficient duration and physiological continuity to ensure neurocognitive performance while we are waking. Insufficient sleep would lead to high risk of human-error related to accidents, injuries or even fatal outcomes. However, in modern society, more and more people suffer from sleep deprivation because of the increasing...
Show moreThe comprehensive effect of sleep deprivation on biological and behavioral functions largely remains unknown. There is evidence to support that human sleep must be of sufficient duration and physiological continuity to ensure neurocognitive performance while we are waking. Insufficient sleep would lead to high risk of human-error related to accidents, injuries or even fatal outcomes. However, in modern society, more and more people suffer from sleep deprivation because of the increasing social, academic or occupational demand. It is important to study the effect of sleep deprivation, not only on task performance, but also on neurocognitive functions. Recent research that has explored brain effective connectivity has demonstrated the directed inference interaction among pairs of brain areas, which may bring important insight to understand how brain works to support neurocognitive function. This research aimed to identify the brain effective connectivity pattern associated with changes of a task performance, response time, following sleep deprivation. Experiments were conducted by colleagues at Neuroergonomics Department at Jagiellonian University, Krakow, Poland. Ten healthy young women, with an average age of 23-year-old, performed visual spatial sustained-attention tasks under two conditions: (1) the rest-wakeful (RW) condition, where participants had their usual sleep and (2) the sleep-deprived (SD) condition, where participants had 3 hours less sleep than their usual sleep, for 7 nights (amounting to 21 h of sleep debt).Measures included eye tracking performance and functional magnetic resonance imaging (fMRI). In each condition, each subject's eye-position was monitored through 13 sessions, each with 46 trials, while fMRI data was recorded. There were two task performance measures, accuracy and response time. Accuracy measured the proportion of correct responses of all trials in each session. Response time measured the average amount of milliseconds until participants gazed at the target stimuli in each session. An experimental session could be treated as a short window. By splitting long trials of fMRI data into consecutive windows, Granger causality was applied based on short trials of fMRI data. This procedure helped to calculate pairwise causal influences with respect to time-varying property in brain causal interaction. Causal influence results were then averaged across sessions to create one matrix for each participant. This matrix was averaged within each condition to formulate a model of brain effective connectivity, which also served as a basis of comparison. In conclusion, significant effect of sleep deprivation was found on response time and brain effective connectivity. In addition, the change of brain effective connectivity after sleep deprivation was linked to the change of response time. First, an analysis of variance (ANOVA) showed significant difference for response time between the RW condition and the SD condition. No significant changes for accuracy were found. A paired t-test showed that response time was significantly shorter in sleep deprivation for the visual spatial sustained-attention task. Second, Granger causality analysis demonstrated a reduction of bidirectional connectivity and an increase of directed influences from low-level brain areas to high-level brain areas after sleep deprivation. This observation suggested that sleep deprivation provoked the effective connectivity engaged in salient stimuli processing, but inhibited the effective connectivity in biasing selection of attention on task and in maintaining self-awareness in day time. Furthermore, in the SD condition, attention at the visual spatial task seemed to be driven by a bottom-up modulation mechanism. Third, a relationship was found between brain effective connectivity with response time. Decreases of Granger causal influences in two directions, from medial frontal lobe to sub cortical gray nuclei and from medial parietal lobe to sub cortical gray nuclei, were associated with shorter response time in the SD condition. Additionally, an increase of Granger causal influence from medial parietal lobe to cerebellum was associated with longer response time in the SD condition.
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Date Issued
2014
Identifier
CFE0005825, ucf:50922
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005825
A deep learning approach to diagnosing schizophrenia
Title
A deep learning approach to diagnosing schizophrenia.
Creator
Barry, Justin, Valliyil Thankachan, Sharma, Gurupur, Varadraj, Jha, Sumit Kumar, Ewetz, Rickard, University of Central Florida
Abstract / Description
In this article, the investigators present a new method using a deep learning approach to diagnose schizophrenia. In the experiment presented, the investigators have used a secondary dataset provided by National Institutes of Health. The aforementioned experimentation involves analyzing this dataset for existence of schizophrenia using traditional machine learning approaches such as logistic regression, support vector machine, and random forest. This is followed by application of deep...
Show moreIn this article, the investigators present a new method using a deep learning approach to diagnose schizophrenia. In the experiment presented, the investigators have used a secondary dataset provided by National Institutes of Health. The aforementioned experimentation involves analyzing this dataset for existence of schizophrenia using traditional machine learning approaches such as logistic regression, support vector machine, and random forest. This is followed by application of deep learning techniques using three hidden layers in the model. The results obtained indicate that deep learning provides state-of-the-art accuracy in diagnosing schizophrenia. Based on these observations, there is a possibility that deep learning may provide a paradigm shift in diagnosing schizophrenia.
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Date Issued
2019
Identifier
CFE0007429, ucf:52737
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007429
Phonon Modulation by Polarized Lasers for Material Modification
Title
Phonon Modulation by Polarized Lasers for Material Modification.
Creator
Chen, Sen-Yong, Kar, Aravinda, Vaidyanathan, Rajan, Harvey, James, Likamwa, Patrick, University of Central Florida
Abstract / Description
Magnetic resonance imaging (MRI) has become one of the premier non-invasive diagnostic tools, with around 60 million MRI scans applied each year. However, there is a risk of thermal injury due to radiofrequency (RF) induction heating of the tissue and implanted metallic device for the patients with the implanted metallic devices. Especially, MRI of the patients with implanted elongated devices such as pacemakers and deep brain stimulation systems is considered contraindicated. Many efforts,...
Show moreMagnetic resonance imaging (MRI) has become one of the premier non-invasive diagnostic tools, with around 60 million MRI scans applied each year. However, there is a risk of thermal injury due to radiofrequency (RF) induction heating of the tissue and implanted metallic device for the patients with the implanted metallic devices. Especially, MRI of the patients with implanted elongated devices such as pacemakers and deep brain stimulation systems is considered contraindicated. Many efforts, such as determining preferred MRI parameters, modifying magnetic field distribution, designing new structure and exploring new materials, have been made to reduce the induction heating. Improving the MRI-compatibility of implanted metallic devices by modifying the properties of the existing materials would be valuable.To evaluate the temperature rise due to RF induction heating on a metallic implant during MRI procedure, an electromagnetic model and thermal model are studied. The models consider the shape of RF magnetic pulses, interaction of RF pulses with metal plate, thermal conduction inside the metal and the convection at the interface between the metal and the surroundings. Transient temperature variation and effects of heat transfer coefficient, reflectivity and MRI settings on the temperature change are studied.Laser diffusion is applied to some titanium sheets for a preliminary study. An electromagnetic and thermal model is developed to choose the proper diffusant. Pt is the diffusant in this study. An electromagnetic model is also developed based on the principles of inverse problems to calculate the electromagnetic properties of the metals from the measured magnetic transmittance. This model is used to determine the reflectivity, dielectric constant and conductivity of treated and as-received Ti sheets. The treated Ti sheets show higher conductivity than the as-received Ti sheets, resulting higher reflectivity.A beam shaping lens system which is designed based on vector diffraction theory is used in laser diffusion. Designing beam shaping lens based on the vector diffraction theory offers improved irradiance profile and new applications such as polarized beam shaping because the polarization nature of laser beams is considered. Laser Pt diffusion are applied on the titanium and tantalum substrates using different laser beam polarizations. The concentration of Pt and oxygen in those substrates are measured using Energy Dispersive X-Ray Spectroscopy (EDS). The magnetic transmittance and conductivity of those substrates are measured as well. The effects of laser beam polarizations on Pt diffusion and the magnetic transmittance and conductivity of those substrates are studied. Treated Ti sheets show lower magnetic transmittance due to the increased conductivity from diffused Pt atoms. On the other hand, treated Ta sheets show higher magnetic transmittance due to reduced conductivity from oxidation. Linearly polarized light can enhance the Pt diffusion because of the excitation of local vibration mode of atoms.Laser Pt diffusion and thermo-treatment were applied on the Ta and MP35N wires. The Pt concentration in laser platinized Ta and MP35N wires was determined using EDS. The ultimate tensile strength, fatigue lives and lead tip heating in real MRI environment of those wires were measured. The lead tip hating of the platinized Ta wires is 42 % less than the as-received Ta wire. The diffused Pt increases the conductivity of Ta wires, resulting in more reflection of magnetic field. In the case of the platinized MP35N wire, the reduction in lead tip heating was only 1 (&)deg;C due to low concentration of Pt. The weaker ultimate tensile strength and shorter fatigue lives of laser-treated Ta and MP35N wires may attribute to the oxidation and heating treatment.
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Date Issued
2012
Identifier
CFE0004500, ucf:49269
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004500
SIGNAL PROCESSING OF AN ECG SIGNALIN THE PRESENCE OF A STRONG STATIC MAGNETIC FIELD
Title
SIGNAL PROCESSING OF AN ECG SIGNALIN THE PRESENCE OF A STRONG STATIC MAGNETIC FIELD.
Creator
Gupta, Aditya, Weeks, Arthur, University of Central Florida
Abstract / Description
This dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect...
Show moreThis dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect diseases such as myocardial infarction. This dissertation aims at finding a solution to the problem of elevation of the T wave of an ECG signal in the MRI. The first step is to simulate the entire situation and obtain the magnetic field dependent T wave elevation. This is achieved by building a model of the aorta and simulating the blood flow in it. This model is then subjected to a static magnetic field and the surface potential on the thorax is measured to observe the T wave elevation. The various parameters on which the T wave elevation is dependent are then analyzed. Different approaches are used to reduce this T wave elevation problem. The direct approach aims at computing the magnitude of T wave elevation using magneto-hydro-dynamic equations. The indirect approach uses digital signal processing tools like the least mean square adaptive filter to remove the T wave elevation and obtain artifact free ECG signal in the MRI. Excellent results are obtained from the simulation model. The model perfectly simulates the ECG signal in the MRI at all the 12 leads of the ECG. These results are compared with ECG signals measured in the MRI. A simulation package is developed in MATLAB based on the simulation model. This package is a graphical user interface allowing the user to change the strength of magnetic field, the radius of the aorta and the orientation of the aorta with respect to the heart and observe the ECG signals with the elevation at the 12 leads of the ECG. Also the artifacts introduced due to the magnetic field can be removed by the least mean square adaptive filter. The filter adapts the ECG signal in the MRI to the ECG signal of the patient outside the MRI. Before the adaptation, the heart rate of the ECG outside the MRI is matched to the ECG in the MRI by interpolation or decimation. The adaptive filter works excellently to remove the T wave artifacts. When the cardiac output of the patient changes, the simulation model is used along with the adaptive filter to obtain the artifact free ECG signal.
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Date Issued
2007
Identifier
CFE0001857, ucf:47389
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001857