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.
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.
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| Ghodasara, Satyam; Pahwa, Shivani; Dastmalchian, Sara et al. (2017) Free-Breathing 3D Liver Perfusion Quantification Using a Dual-Input Two-Compartment Model. Sci Rep 7:17502 |
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