Uzay Emirمشاهده پروفایل
دانشیار
- MRI/MRS Methodology
- Neurodegenerative Disease Biomarkers
- Advanced Imaging Sequences (UTE, Rosette)
- +۳ مورد دیگر
Uzay Emir is a Joint Associate Professor in Radiology at UNC-Chapel Hill, with cross-institutional roles including Principal Investigator at the University of Oxford and prior experience as an Assistant Professor at Purdue University. His research focuses on advancing MRI/MRS methodologies for neurodegenerative disease biomarker discovery, particularly using ultra-short echo time (UTE) and Rosette trajectory-based imaging techniques. Emir's work emphasizes translational applications across preclinical and clinical settings, including 3T to 9.4T field strengths. He pioneered the PETALUTE sequence for accelerated phosphorus spectroscopic imaging and led the multicenter 'Repeat it with me challenge' for test-retest reproducibility in Rosette MRI(S)I. His innovations include density-weighted concentric ring trajectories and 3D Rosette-based methods for brain iron content mapping and myelin fraction analysis. Research interests span neurochemical profiling, metabolic imaging, and functional MRI-fMRS integration at 7T. Education: PhD in imaging modalities (fMRI signal transients), postdoctoral training in MRS methods at the University of Minnesota's Center for Magnetic Resonance Research Key Methods: UTE MRI/MRSI, Rosette trajectory, 31P-MRSI, PETALUTE sequence Key Projects: ME/CFS metabolic studies, lead toxicity neuroimaging, sodium cartilage quantification Research trends in his articles highlight development of novel imaging sequences (e.g., ZTE fMRI, accelerated J-resolved spectroscopy) and their application to neurological disorders. Emphasis on clinical feasibility of 31P-MRS after decades of technical challenges underscores his translational impact. Recent work includes simultaneous multi-slice MRSI and NIfTI-MRS data standardization efforts. His contributions bridge preclinical-clinical research through standardized protocols enabling biomarker validation. Current efforts explore spatiotemporal dynamics of neural networks using integrated fMRI-fMRS approaches.







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