Ultrahigh Field Magnetic Resonance Imaging – Technical Development and Translational Applications

<p>Magnetic resonance imaging (MRI) may be used to provide detailed images of the human body with excellent soft tissue contrast. Alongside its current widespread clinical applications for diagnosis and treatment, MRI allows researchers to measure structure and function of different tissue types in order to advance our understanding of human biology and enable new medical applications of MRI. In particular, diseases affecting nerves and vessels, such as trigeminal neuralgia, with uncertain etiology can be studied using multiple MRI modalities so that treatment planning can we more effective and patient outcomes can be improved. Ultrahigh field MRI scanners, such as those operating at 7-­‐tesla (7T), provide increased signal-­‐to-­‐noise ratio, which can be translated to higher spatial resolution. Additional advantages of high magnetic field MRI include enhanced vascular contrast as well as improved spectral separation and quantification for MR spectroscopy. These benefits over MRI at lower field strengths make ultrahigh field MRI a powerful new tool for performing quantitative image analysis with increased accuracy. One quantitative application of MRI is the detection and visualization of cells labeled with magnetic nanoparticles. This unconventional use of the imaging modality enables very effective imaging of cells or lesions tagged with these particles. The projects explored herein consist of such quantitative image analysis using advanced imaging techniques, including ultrahigh field MRI.</p>

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Biomedical Engineering

Year dc:date.available

2016

Author dc:creator

• Alper, Judy

• Magnetic resonance imaging (MRI)
• Trigeminal neuralgia
• Nanoparticles
• Biomedical Engineering and Bioengineering

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/636

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-1636