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Articles 1 - 5 of 5
Full-Text Articles in Biomedical Engineering and Bioengineering
Incorporating The Aortic Valve Into Computational Fluid Dynamics Models Using Phase-Contrast Mri And Valve Tracking, David C. Wendell
Incorporating The Aortic Valve Into Computational Fluid Dynamics Models Using Phase-Contrast Mri And Valve Tracking, David C. Wendell
Dissertations (1934 -)
The American Heart Association states about 2% of the general population have a bicuspid aortic valve (BAV). BAVs exist in 80% of patients with aortic coarctation (CoA) and likely influences flow patterns that contribute to long-term morbidity post-surgically. BAV patients tend to have larger ascending aortic diameters, increased risk of aneurysm formation, and require surgical intervention earlier than patients with a normal aortic valve. Magnetic resonance imaging (MRI) has been used clinically to assess aortic arch morphology and blood flow in these patients. These MRI data have been used in computational fluid dynamics (CFD) studies to investigate potential adverse hemodynamics …
Comparison Of Progression Of Diffuse Axonal Injury With Histology And Diffusion Tensor Imaging, Nisrine Zakaria
Comparison Of Progression Of Diffuse Axonal Injury With Histology And Diffusion Tensor Imaging, Nisrine Zakaria
Wayne State University Dissertations
Diffuse axonal injury, also known as traumatic axonal injury (TAI), is a major contributor to the pathology of traumatic brain injury. However, TAI is undetectable to conventional clinical magnetic resonance (MR) imaging techniques. Histologically, TAI is characterized by swollen axons that eventually disconnect and form axonal retraction balls (RB) in various white matter tracts. MR-diffusion tensor imaging (MR-DTI) has been reported to be sensitive to TAI in human TBI patients by measuring water molecular diffusion motion in white matter fiber tracts. To date, only one correlative animal study has been carried out to investigate the DTI relationship to TAI, and …
A Left Ventricular Motion Phantom For Cardiac Magnetic Resonance Imaging, Mehmet Ersoy
A Left Ventricular Motion Phantom For Cardiac Magnetic Resonance Imaging, Mehmet Ersoy
ETD Archive
The mammalian left ventricle (LV) has two distinct motion patterns: wall thickening and rotation. The purpose of this study was to design and build a low-cost, non-ferromagnetic LV motion phantom, for use with cardiac magnetic resonance imaging (MRI), that is able to produce physiologically realistic LV wall thickening and rotation. Cardiac MRI is continuously expanding its range of techniques with new pulse sequences, including new tissue tagging techniques which allow intra-myocardial deformation to be visualized. An essential step in the development of new cardiac MRI techniques is validating their performance in the presence of motion. MRI-compatible dynamic motion phantoms are …
Magnetic Resonance Image Processing Using Levy, Jonathan Blackledge, Matthew D. Blackledge
Magnetic Resonance Image Processing Using Levy, Jonathan Blackledge, Matthew D. Blackledge
Conference papers
We consider the physical nature of the self-diffusion of water molecules in tissue and explore how (Nuclear) Magnetic Resonance (MR) imaging may be used as a means of measuring the rate of diffusion in vivo. A discussion is presented on how these techniques may be implemented as a non-invasive means of assessing the response of tumours to novel therapeutics including some of the basic advantages and disadvan- tages when compared to other methods. The physical basis and mathematical models for diffusion are considered together with models for the distribution of the diffusion co- efficient including a Lévy distributed model. Using …
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.)
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 …