The MRF concept presented here is a new approach to magnetic resonance and provides many opportunities to extend such measurements beyond their current limits. This originates from the unique pulse sequence design concept in MRF, where the goal is to generate unique signal evolutions that can be matched to theoretical signal evolutions and subsequently yield underlying quantitative information about the material, tissue or pathology of interest. Because there is no a priori requirement on the shape of the signal evolution curves, there are more degrees of freedom in designing an MRF acquisition, where parameters such as repetition time, echo time, radio frequency pulses and sampling trajectories (among others) can be varied together to produce the simultaneous sensitivity to numerous tissue properties. The ability to analyse oscillating signals in MRF also provides the opportunity to use larger fractions of the available magnetization than methods that rely on a steady-state signal, which is a significant factor contributing to the higher efficiency in MRF. In addition, the oscillatory signal in MRF allows one to sample more informative points along a longer signal evolution as compared to conventional methods which always reach a steady state level after some finite amount of time. Specifically, our initial results here demonstrate that the efficiency of MRF is approximately 1.8 times higher than the DESPOT methods, which were previously the most efficient methods for the measurement of relaxation parameters. Thus the direct prediction of the oscillating, incoherent signal evolutions through the Bloch simulation provides us the potential to obtain new quantitative information that is impractical today because of the prohibitively long scan times required, especially in biological samples and patients.

As demonstrated by the results shown here, MRF has the potential to significantly reduce the effects of errors during acquisition through its basis in pattern recognition. Acquisition errors may globally reduce the probability of a match of an observed signal to any given fingerprint, but as long as the errors do not cause another fingerprint to become the most likely match, the correct quantitative identification will still be made. Ideally, the sequence pattern will be designed so that the various fingerprints from different tissues and materials are as independent as possible, thus ensuring this robustness against motion and other practical errors.

Commercial magnetic resonance scanners include methods to minimize the effects of unavoidable system imperfections. However, these inaccuracies are becoming increasingly important as magnetic resonance technology is pushed to its limits, such as the use of very high magnetic fields or physically larger systems. MRF provides a route to model and account for system imperfections, such as inhomogeneities in both the static magnetic field (B 0 ) and the applied radio frequency field (B 1 ), by adding these parameters into the dictionary simulation. Because both MRF and DESPOT2 are based on a bSSFP sequence, which is known to be sensitive to field inhomogeneities30,37, Supplementary Fig. 2 compares the T 2 maps acquired from MRF and DESPOT2 from an in vivo scan. Because off-resonance is not taken into account in the DESPOT2 model, the T 2 map from DESPOT2 shows areas of signal voids resulting from susceptibility effects at the air–tissue interfaces. MRF naturally incorporates these effects into the fingerprints, and thus the maps generated by MRF do not show these errors. Thus MRF could, for example, provide higher quality results using the current generation of magnetic resonance scanners. Alternatively, MRF could also allow the design of lower cost magnetic resonance scanners that can provide the same quality as today’s high end systems through application of MRF models.

Because of its ability to provide quantitative results across many parameters simultaneously, MRF could lead to the direct identification of a material, tissue or pathology solely on the basis of its fingerprint. For example, many cancer cells show changes in multiple magnetic resonance parameters (for example, T 1 , T 2 and self-diffusion tensor), a combination (though no single parameter) of which could potentially characterize them as different from all surrounding normal tissue types, and thus potentially separable. In an ideal situation, each given material, tissue or pathology would have its own signal evolution which would be orthogonal to all other signal evolutions. The MRF concept also implies that completely different acquisition schemes are possible in cases where one is only interested in the presence or absence of a particular material or disease state. For example, one could do a very rapid MRF scan of a bulk area of material or tissue and compare the measured signal evolutions against the set of known states of interest. This measurement could either indicate the presence of the material or disease of interest, or indicate its absence within a margin of error. This feature could result in very rapid and accurate screening procedures. In particular, this feature may help to relax the required spatial resolution of an MRI examination, thus increasing the speed, and potentially reducing the cost, of such an examination. A preliminary example of this kind of visualization is shown in Supplementary Information Section 3. Using the MRF concept, the operation of the magnetic resonance unit will also be greatly simplified, because the ‘all in one’ scan concept of MRF has the potential to reduce the dozens of parameters currently presented to the magnetic resonance operator to a simple ‘scan’ button.

It is important to note that the proof-of-principle implementation of MRF shown here is but one of the many possibilities that could be used for this technique, and both the sequence design/implementation and post-processing methods will continue to be a significant open area of research, just as sequence design has advanced over the decades since the conventional methods have been introduced. Other, more advanced pattern recognition algorithms38,39,40,41,42 will probably improve the performance of MRF. For spatially encoded MRI applications, the parameters retrieved from MRF are far fewer than the number of pixels in the images, and because the signals generated are largely incoherent, MRF has the additional potential to be highly accelerated through combination with other compressed sensing methods for accelerated spatial encoding, in addition to the now standard parallel imaging methods43,44,45, neither of which were included here. Any of these methods would reduce the undersampling errors seeing in Fig. 3a even before the pattern recognition step, which should result in higher quality results. We have recently published data indicating that we can achieve a reduction in imaging time of about ten times for a two-dimensional slice using parallel imaging alone43,45,46. Also, it should be noted that the proof-of-principle results shown here only take advantage of two spatial dimensions for undersampling, whereas it is well known that taking full advantage of undersampling in all three spatial dimensions gives higher performance than a two-dimensional acquisition owing to the reduced power of the resulting errors at any given undersampling factor47. Thus a combination of an optimized three-dimensional MRF pulse sequence with parallel imaging and more advanced pattern matching algorithms will allow realization MRF in very short scan times.