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- Title
- A STRUCTURAL AND FUNCTIONAL ANALYSIS OF HUMAN BRAIN MRI WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER.
- Creator
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Watane, Arjun A, Bagci, Ulas, University of Central Florida
- Abstract / Description
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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.
Show less - Date Issued
- 2017
- Identifier
- CFH2000203, ucf:45978
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000203
- Title
- Learning Algorithms for Fat Quantification and Tumor Characterization.
- Creator
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Hussein, Sarfaraz, Bagci, Ulas, Shah, Mubarak, Heinrich, Mark, Pensky, Marianna, University of Central Florida
- Abstract / Description
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Obesity is one of the most prevalent health conditions. About 30% of the world's and over 70% of the United States' adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the...
Show moreObesity is one of the most prevalent health conditions. About 30% of the world's and over 70% of the United States' adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice.
Show less - Date Issued
- 2018
- Identifier
- CFE0007196, ucf:52288
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007196
- Title
- TEACHING AND ASSESSING CRITICAL THINKING IN RADIOLOGIC TECHNOLOGY STUDENTS.
- Creator
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Gosnell, Susan, Biraimah, Karen, University of Central Florida
- Abstract / Description
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The purpose of this study was primarily to explore the conceptualization of critical thinking development in radiologic science students by radiography program directors. Seven research questions framed three overriding themes including 1) perceived definition of and skills associated with critical thinking; 2) effectiveness and utilization of teaching strategies for the development of critical thinking; and 3) appropriateness and utilization of specific assessment measures for documenting...
Show moreThe purpose of this study was primarily to explore the conceptualization of critical thinking development in radiologic science students by radiography program directors. Seven research questions framed three overriding themes including 1) perceived definition of and skills associated with critical thinking; 2) effectiveness and utilization of teaching strategies for the development of critical thinking; and 3) appropriateness and utilization of specific assessment measures for documenting critical thinking development. The population for this study included program directors for all JRCERT accredited radiography programs in the United States. Questionnaires were distributed via Survey Monkey©, a commercial on-line survey tool to 620 programs. A forty-seven percent (n = 295) response rate was achieved and included good representation from each of the three recognized program levels (AS, BS and certificate). Statistical analyses performed on the collected data included descriptive analyses (median, mean and standard deviation) to ascertain overall perceptions of the definition of critical thinking; levels of agreement regarding the effectiveness of listed teaching strategies and assessment measures; and the degree of utilization of the same teaching strategies and assessment measures. Chi squared analyses were conducted to identify differences within each of these themes between various program levels and/or between program directors with various levels of educational preparation as defined by the highest degree earned. Results showed that program directors had a broad and somewhat ambiguous perception of the definition of critical thinking, which included many related cognitive processes that were not always classified as attributes of critical thinking according to the literature, but were consistent with definitions and attributes identified as critical thinking by other allied health professions. These common attributes included creative thinking, decision making, problem solving and clinical reasoning as well as other high-order thinking activities such as reflection, judging and reasoning deductively and inductively. Statistically significant differences were identified for some items based on program level and for one item based on program director highest degree. There was general agreement regarding the appropriateness of specific teaching strategies also supported by the literature with the exception of on-line discussions and portfolios. The most highly used teaching strategies reported were not completely congruent with the literature and included traditional lectures with in-class discussions and high-order multiple choice test items. Significant differences between program levels were identified for only two items. The most highly used assessment measures included clinical competency results, employer surveys, image critique performance, specific course assignments, student surveys and ARRT exam results. Only one variable showed significant differences between programs at various academic levels.
Show less - Date Issued
- 2010
- Identifier
- CFE0003261, ucf:48518
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003261
