Medicine and Health Sciences Commons^™

Magnetic Resonance Imaging Sequence Identification Using A Metadata Learning Approach, Shuai Liang, Derek Beaton, Stephen R. Arnott, Tom Gee, Mojdeh Zamyadi, Robert Bartha, Sean Symons, Glenda M. Macqueen, Stefanie Hassel, Jason P. Lerch, Evdokia Anagnostou, Raymond W. Lam, Benicio N. Frey, Roumen Milev, Daniel J. Müller, Sidney H. Kennedy, Christopher J.M. Scott, Stephen C. Strother

Medical Biophysics Publications

Despite the wide application of the magnetic resonance imaging (MRI) technique, there are no widely used standards on naming and describing MRI sequences. The absence of consistent naming conventions presents a major challenge in automating image processing since most MRI software require a priori knowledge of the type of the MRI sequences to be processed. This issue becomes increasingly critical with the current efforts toward open-sharing of MRI data in the neuroscience community. This manuscript reports an MRI sequence detection method using imaging metadata and a supervised machine learning technique. Three datasets from the Brain Center for Ontario Data Exploration …

Pairwise Correlation Analysis Of The Alzheimer’S Disease Neuroimaging Initiative (Adni) Dataset Reveals Significant Feature Correlation, Erik D. Huckvale, Matthew W. Hodgman, Brianna B. Greenwood, Devorah O. Stucki, Katrisa M. Ward, Mark T. W. Ebbert, John S. K. Kauwe, The Alzheimer’S Disease Neuroimaging Initiative, The Alzheimer’S Disease Metabolomics Consortium, Justin B. Miller

Sanders-Brown Center on Aging Faculty Publications

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the …

Public Discussion Of Anthrax On Twitter: Using Machine Learning To Identify Relevant Topics And Events, Michele Miller, William Lee Romine, Terry L. Oroszi

Biological Sciences Faculty Publications

Background: Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events. Objective: The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of thetweets and topics of discussion over 12 months of data collection. Methods: This is an infoveillance study, using tweets in English containing the keyword “Anthrax” and …

Computer-Assisted Lesion Classification And Intervention Planning For Prostate Cancer, Ryan M. Alfano

Electronic Thesis and Dissertation Repository

Multi-parametric magnetic resonance imaging (mp-MRI) is emerging as a useful tool for classifying prostate cancer (PCa); however, it suffers from two major limitations: (1) complex, multi-dimensional signals make interpretation challenging and (2) inter-observer variability of lesion classification between physicians. Critically needed are methods for augmenting the interpretability of mp-MRI to assist in lesion classification. To meet this need, we leveraged a patient cohort with post-surgery pathologist-annotated transverse histology images registered to pre-surgery in-vivo mp-MRI with a measured target registration error. We developed a radiomics-based machine learning model trained on annotations for PCa vs. non-PCa, and found that a 5-feature Naïve-Bayes …

Imaging Based Prediction Of Pathology In Adult Diffuse Glioma With Applications To Therapy And Prognosis, Evan Gates

Dissertations & Theses (Open Access)

The overall aggressiveness of a glioma is measured by histologic and molecular analysis of tissue samples. However, the well-known spatial heterogeneity in gliomas limits the ability for clinicians to use that information to make spatially specific treatment decisions. Magnetic resonance imaging (MRI) visualizes and assesses the tumor. But, the exact degree to which MRI correlates with the actual underlying tissue characteristics is not known.

In this work, we derive quantitative relationships between imaging and underlying pathology. These relations increase the value of MRI by allowing it to be a better surrogate for underlying pathology and they allow evaluation of the …

Investigating Diffusion Tensor Imaging Correlates Of Cognitive Impairment In Idiopathic Normal Pressure Hydrocephalus And Alzheimer's Disease, Omar Hasan, Omar Hasan

Dissertations & Theses (Open Access)

Modest expansion of the human brain cerebrospinal fluid (CSF)-filled ventricles is normal with aging, and because of this, it can be difficult for physicians to accurately diagnose and treat enlarged ventricles (ventriculomegaly), called hydrocephalus¹ (fluid or water in the brain) Ventriculomegaly occurs due to an obstruction (such as a blood clot or tumor), or a change in CSF absorption². Primary hydrocephalus, also called idiopathic normal pressure hydrocephalus (iNPH), is non-obstructive and may be comorbid with other neurodegenerative diseases such as Alzheimer’s disease (AD) or frontotemporal dementia (FTD). Clinically, it can be difficult to tell whether the pathophysiological …