Magnetic resonance fingerprinting

Abstract

Magnetic resonance is an exceptionally powerful and versatile measurement technique. The basic structure of a magnetic resonance experiment has remained largely unchanged for almost 50 years, being mainly restricted to the qualitative probing of only a limited set of the properties that can in principle be accessed by this technique. Here we introduce an approach to data acquisition, post-processing and visualization--which we term 'magnetic resonance fingerprinting' (MRF)--that permits the simultaneous non-invasive quantification of multiple important properties of a material or tissue. MRF thus provides an alternative way to quantitatively detect and analyse complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to identify the presence of a specific target material or tissue, which will increase the sensitivity, specificity and speed of a magnetic resonance study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern-recognition algorithm, MRF inherently suppresses measurement errors and can thus improve measurement accuracy.

Figures

Figure 1. MRF sequence pattern

a, Acquisition sequence diagram. In each TR, various sequence components are varied in a pseudorandom pattern. b, Here, one variable density spiral trajectory was used per TR. The trajectory rotated from one TR to the next. c and d are examples of the first 500 points of FA and TR patterns that were used in this study.

Figure 2. Signal properties and matching results from phantom study

(a and b) Simulated signal evolution curves corresponding to four normal brain tissues using the sequence patterns in Figure 1c and 1d, respectively as a fraction of the equilibrium magnetization. The curve from white matter with off-resonance is also plotted. (c and d) Measured signal evolutions from one of eight phantoms using different sequence patterns and their dictionary match. The estimated T1, T2, and off-resonance are (340 ms, 50 ms, −4 Hz) and (340 ms, 50 ms, −13 Hz) in (c) and (d), respectively. The plots are normalized to their maximum value.

Figure 3. MRF results from highly undersampled data

a. An image at the 5th TR out of 1000 was reconstructed from only 1 spiral readout demonstrating the significant errors from undersampling. b, one example of acquired single evolution and its match to the dictionary. Note the significant interference resulting from the undersampling. The reconstructed parameter maps show a near complete rejection of these errors based solely on the incoherence between the underlying MRF signals and the undersampling errors. (c), T1 map (e) T2 map (d) off-resonance frequency and (f) spin-density (M0) map. These data required 12.3 seconds to acquire.

Figure 4. Demonstration of error tolerance in the presence of motion

Reconstructed images acquired at the 12th second (a) and at the 15th second (b) demonstrate the large shift in the head position. The resulting MRF maps are nearly identical, demonstrating a rejection of both undersampling and motion errors that are uncorrelated with the expected signal evolution. (T1 map (c) and T2 map (e) from the first 12 seconds that has no motion, T1 map (d) and T2 map (f) from entire 15 seconds that includes the motion).

Figure 5. Accuracy, Efficiency and Error estimation for MRF and DESPOT

The T1 and T2 values retrieved from MRF from eight phantoms were compared with those acquired from DESPOT1(a), DESPOT2(b) and a standard spin-echo sequence. The efficiency of MRF was compared to DESPOT1(c) and DESPOT2(d) at different T1 and T2 values. MRF has an average of 1.87 and 1.85 times higher efficiency than DESPOT1 and DESPOT2, respectively. (e) and (f) show the means and standard deviations of T1 and T2 as a function of acquisition time. Error bars represent the standard deviations of the results over a 25-pixel region in the center of each phantom, which are smaller than the symbols for most MRF results.