MRI has become a key component of clinical medicine because it offers exquisite images of soft tissues that can be made sensitive to almost any disease process. However, it is increasingly clear that the current paradigm of MRI has run up against significant limitations that are, in many cases, unavoidable. Current clinical MRI is effective but practically restricted to a qualitative depiction of a limited set of tissue propertis which are visualized through a series of different acquisitions. Further, the reliance on human interpretation limits the total amount of information that can be assessed in any given exam. Here we propose a radically different paradigm that could dramatically increase the efficiency and specificity of MRI by taking a completely different approach to image acquisition, post-processing and visualization. This new class of methods, Magnetic Resonance Fingerprinting (MRF), overcomes traditional limitations by fully embracing the concept of signal incoherence at the core of compressed sensing. Because of the richness of the parameters that can be analyzed, MR- Fingerprinting methods could augment current interpretation to improve diagnosis and monitoring of disease. Indeed, we are proposing an altogether new approach to medical imaging that directly collects anatomically informed quantitative information about disease state and physiology.

Public Health Relevance

Despite exquisite soft tissue visualization and a significant impact on health care in the 40 years since its conceptualization, MRI as it is used today still has significant limitations due to complexity of scanning, inefficient image acquisition, qualitatie rather than quantitative imaging, and complicated image interpretation. We present a new paradigm which we call MR fingerprinting (MRF) in which we overcome these limitations by changing the acquisition, post-processing, and by extension interpretation of MR images. MRF represents an altogether new approach to medical imaging that directly collects anatomically informed quantitative information about disease state and physiology.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB016728-01A1
Application #
8696434
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Liu, Guoying
Project Start
2014-03-15
Project End
2018-02-28
Budget Start
2014-03-15
Budget End
2015-02-28
Support Year
1
Fiscal Year
2014
Total Cost
$509,829
Indirect Cost
$178,694
Name
Case Western Reserve University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
077758407
City
Cleveland
State
OH
Country
United States
Zip Code
44106