Time is money for the provider, and both mental and physical convenience for the patient. Nobody wants to be in a scanner longer than needed, but a provider needs to come to a proper diagnosis. MRI provides a large amount of possible measures, many of which with biomarker potential. A choice of scans often has to be made, which may lead to incomplete diagnosis. Recently, a strategy called ‘‘MR fingerprinting (MRF)” was introduced (1,2) in which the most time-efficient and optimal variation of multiple pulse sequence parameters is designed to acquire an under-sampled data set. Tissue parameters are then quantified by comparing this minimal dataset with a simulated library of signal variation patterns based on the sequence used. The analysis portion of MRF is expected to be readily combined with or replaced by deep learning technologies and many MRF approaches are expected to become available in the near future both for existing MRI sequences and new combinations of sequences. There is a great expectation that multiple biomarkers useful for the clinic will come out of this, which can then be combined with markers from other imaging or clinical markers for precision medicine. In addition, the availability of these parameters can be used to synthetically generate other image contrasts with which the radiologists are familiar and that are now being acquired separately, a field called synthetic MRI (3). This trend of time savings combined with availability of more information will likely become the standard in clinical MRI. We are designing MRF pulse sequences to determine microscopic tissue properties (diffusion and exchange based), T1, T2, T2*, and magnetization transfer processes (e.g. 4-6), such as semi-solid macromolecule based magnetization transfer contrast (MTC) or chemical exchange saturation transfer (CEST). An example for MTC-MRF is given in the figure, allowing several tissue parameters to be quantified simultaneously and mapped: water T1 (T1w), semi-solid macromolecular proton exchange rate (kmw), concentration of semisolid macromolecular protons (M0m), T2 of semisolid macromolecular protons (T2m)
(1) D. Ma, V. Gulani, N. Seiberlich, K. Liu, J. L. Sunshine, J. L. Duerk and M. A. Griswold, Magnetic resonance fingerprinting. Nature, 2013, 495, 187-192
(2) A. Panda, B. B. Mehta, S. Coppo, Y. Jiang, D. Ma, N. Seiberlich, M. A. Griswold and V. Gulani, Magnetic Resonance Fingerprinting-An Overview. Curr Opin Biomed Eng, 2017, 3, 56-66
(3) F. G. Goncalves, S. D. Serai and G. Zuccoli, Synthetic Brain MRI: Review of Current Concepts and Future Directions. Top Magn Reson Imaging, 2018, 27, 387-393
(4) H. Y. Heo, X. Xu, S. Jiang, Y. Zhao, J. Keupp, K. J. Redmond, J. Laterra, P. C. M. van Zijl and J. Zhou, Prospective acceleration of parallel RF transmission-based 3D chemical exchange saturation transfer imaging with compressed sensing. Magn Reson Med, 2019, 82, 1812-1821
(5) B. Kim, M. Schar, H. Park and H. Y. Heo, A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging. Neuroimage, 2020, 221, 117165
(6) H. Y. Heo, Z. Han, S. Jiang, M Schar, P.C.M. van Zijl and J. Zhou, Quantifying amide proton exchange rate and concentration in chemical exchange saturation transfer imaging of the human brain. Neuroimage 2019,189,202-213.
