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MR Fingerprinting Based Quantitative Imaging and Analysis Platform (MRF-QIA) for Brain Tumors

MR Fingerprinting Based Quantitative Imaging and Analysis Platform (MRF-QIA) for Brain Tumors

Academic industry partnership to translate quantitative imaging and analysis into the clinical workflow

Funding and Collaborators: NIH NCI, Siemens, University Hospitals Cleveland Medical Center, University of Pennsylvania

Making the Invisible Visible

Making the Invisible Visible

A multi-scale imaging approach to detect and characterize cortical pathology

Funding and Collaborators: UKRI MRC, Cardiff University

Clinically Feasible MR Fingerprinting Imaging Framework 

Clinically Feasible MR Fingerprinting Imaging Framework 

- Simultaneous T1 and T2 mapping with whole brain coverage and isotropic image resolution
- Sequence optimization using quantum optimization algorithms 
- Low rank subspace image reconstruction
- Partial volume analysis and tissue segmentation

Funding and Collaborators: Siemens, Microsoft

MR Fingerprinting in Epilepsy

MR Fingerprinting in Epilepsy

Epilepsy affects 65 million people worldwide; approximately 30% of them do not respond to medications but can be cured by surgery. Focal cortical dysplasia, a major pathology for medically intractable epilepsies, are frequently missed by visual analysis of the conventional MRI, making surgical treatment very difficult. Here we propose to develop and validate novel, noninvasive and quantitative MRI acquisition and post-processing techniques, in order to guide epilepsy surgery and make more patients seizure-free.

Funding and Collaborators: NIH NINDS, Cleveland Clinic Epilepsy Center

A Framework to Design 3D MRF Scans and Reduce Patient Anxiety

A Framework to Design 3D MRF Scans and Reduce Patient Anxiety

Loud noise during MRI scans is the leading cause of patients’ anxiety, but the origin of this loud noise, mainly fast-switching fields, is also an essential component to generate images. Previous methods rely almost solely on slowing down the switching field to reduce the noise, resulting in reduced scan efficiency. We propose a general framework that could resolve this longstanding conflict by changing the sound of the MRI scan to music while simultaneously providing multiple quantitative tissue properties with high scan efficiency.

Funding and Collaborators: NIH NIBIB, University Hospitals Cleveland Medical Center

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