Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

A Multi-Institutional Meningioma Mri Dataset For Automated Multi-Sequence Image Segmentation, Dominic Labella, Omaditya Khanna, Shan Mcburney-Lin, Ryan Mclean, Pierre Nedelec, Arif Rashid, Nourel Hoda Tahon, Talissa Altes, Ujjwal Baid, Radhika Bhalerao, Yaseen Dhemesh, Scott Floyd, Devon Godfrey, Fathi Hilal, Anastasia Janas, Anahita Kazerooni, Collin Kent, John Kirkpatrick, Florian Kofler, Kevin Leu, Nazanin Maleki, Bjoern Menze, Maxence Pajot, Zachary Reitman, Jeffrey Rudie, Rachit Saluja, Yury Velichko, Chunhao Wang, Pranav Warman, Nico Sollmann, David Diffley, Khanak Nandolia, Daniel Warren, Ali Hussain, John Pascal Fehringer, Yulia Bronstein, Lisa Deptula, Evan Stein, Mahsa Taherzadeh, Eduardo Portela De Oliveira, Aoife Haughey, Marinos Kontzialis, Luca Saba, Benjamin Turner, Melanie Brüßeler, Shehbaz Ansari, Athanasios Gkampenis, David Maximilian Weiss, Aya Mansour, Islam Shawali, Nikolay Yordanov, Joel Stein, Roula Hourani, Mohammed Yahya Moshebah, Ahmed Magdy Abouelatta, Tanvir Rizvi, Klara Willms, Dann Martin, Abdullah Okar, Gennaro D'Anna, Ahmed Taha, Yasaman Sharifi, Shahriar Faghani, Dominic Kite, Marco Pinho, Muhammad Ammar Haider, Michelle Alonso-Basanta, Javier Villanueva-Meyer, Andreas Rauschecker, Ayman Nada, Mariam Aboian, Adam Flanders, Spyridon Bakas, Evan Calabrese

Department of Radiology Faculty Papers

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by …

Magnetic Resonance Imaging-Derived Microvascular Perfusion Modeling To Assess Peripheral Artery Disease, Olga A. Gimnich, Tatiana Belousova, Christina M. Short, Addison A. Taylor, Vijay Nambi, Joel D. Morrisett, Christie M. Ballantyne, Jean Bismuth, Dipan J. Shah, Gerd Brunner

School of Medicine Faculty Publications

BACKGROUND: Computational fluid dynamics has shown good agreement with contrast-enhanced magnetic resonance imaging measurements in cardiovascular disease applications. We have developed a biomechanical model of microvascular perfusion using contrast-enhanced magnetic resonance imaging signal intensities derived from skeletal calf muscles to study peripheral artery disease (PAD). METHODS AND RESULTS: The computational microvascular model was used to study skeletal calf muscle perfusion in 56 in-dividuals (36 patients with PAD, 20 matched controls). The recruited participants underwent contrast-enhanced magnetic resonance imaging and ankle-brachial index testing at rest and after 6-minute treadmill walking. We have determined associations of microvascular model parameters including the transfer …

An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma

MSU Graduate Theses

Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. The effectiveness of deep learning models are hampered by limited data sets, skewed class distributions, and the undesirable "black box" of neural networks, which decreases their understandability and usability in precision medicine applications. This thesis addresses the challenge of building an explainable deep learning model for a clinical application: predicting the severity of Alzheimer's disease (AD). AD …

Diagnostic Accuracy Of Mcmurry’S Test In The Diagnosis Of Meniscal Tears, Jessica Smyth, Hartwell Rainey

Physician Assistant Capstones, 2020-current

No abstract provided.

Prediction Of Molecular Mutations In Diffuse Low-Grade Gliomas Using Mr Imaging Features, Zeina A. Shboul, James Chen, Khan M. Iftekharrudin

Electrical & Computer Engineering Faculty Publications

Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We introduce molecular (MGMT methylation, IDH mutation, 1p/19q co-deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance imaging data and include texture features, fractal and multi-resolution fractal texture features, and volumetric features. Training models include …

Investigating In Vivo Brain Metabolite Levels In Concussed Female Athletes And A Murine Model Of Repetitive Closed Head Injury, Amy L. Schranz

Electronic Thesis and Dissertation Repository

After a concussion there is a complex cascade of events, termed the neurometabolic cascade, that includes changes in ion flux, neurotransmission, and cellular energetics. How this pathophysiological process translates into cognitive deficits remains poorly understood. Magnetic resonance spectroscopy (MRS) provides a non-invasive technique that allows for the quantification of brain metabolites that are involved in these processes, including glutamate and glutamine, which are involved in neurotransmission. Moreover, female athletes are underrepresented in studies on concussion, limiting our knowledge and understanding of sex differences. The overall goal of this thesis was to examine metabolite changes using MRS in female athletes before …

An Edge-Directed Interpolation Method For Fetal Spine Mr Images, Shaode Yu, Rui Zhang, Shibin Wu, Jiani Hu, Yaoqin Xie

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Fetal spinal magnetic resonance imaging (MRI) is a prenatal routine for proper assessment of fetus development, especially when suspected spinal malformations occur while ultrasound fails to provide details. Limited by hardware, fetal spine MR images suffer from its low resolution.

High-resolution MR images can directly enhance readability and improve diagnosis accuracy. Image interpolation for higher resolution is required in clinical situations, while many methods fail to preserve edge structures. Edge carries heavy structural messages of objects in visual scenes for doctors to detect suspicions, classify malformations and make correct diagnosis. Effective interpolation with well-preserved edge structures is still …

Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)

Electrical & Computer Engineering Faculty Publications

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans …