MR FingerprintingA giant leap for precision medicine

MR Fingerprinting generates reliable, quantitative multi-parametric maps with high reproducibility that can lay the groundwork for more accurate diagnosis.

MRF acquisition employs an innovative, pseudo-random variation of scan parameters, such as flip angle (FA), repetition time (TR), echo-time (TE), or readout trajectory, to extract multiple quantitative tissue parameters from a single scan. By varying these parameters, tissues create unique signal evolutions of data or “fingerprints,” which reflect the composition of the scanned tissue. MRF utilizes a spiral sampling trajectory, which drastically under-samples the acquired data. This results in a fast and efficient acquisition, while preserving spatial and temporal data integrity.

MR Fingerprinting intends to acquire quantitative maps to accurately measure tissue properties, and describe the target anatomy numerically instead of visually.

Every MRF acquisition sequence has its own Dictionary that is calculated before scanning. The Dictionary is a database of possible unique signal evolutions, or fingerprints. For example, the three properties, T1, T2, and B1, can lead to a Dictionary with over 700,000 entries. A pattern matching process compares the fingerprints with the Dictionary. When there is a match, the properties of this fingerprint are assigned to a map. This process is repeated sequentially until all fingerprints have a corresponding property, creating a 1:1 map.

The objective of MR Fingerprinting is to generate robust, quantitative multi-parametric maps with high reproducibility that are able to measure absolute values.

The Analyzer² simultaneously analyzes all quantitative data from MRF parameter maps, conventional parameter maps, and anatomical MR data. The dependency between two parameters is visualized by a scatter plot for the analysis of the relationship of measured quantitative MRF parameters (such as T1 or T2 values), and conventional quantitative data (such as ADC values). The plot displays the values of one parameter over a second parameter across all voxels of the measured volume. This represents distinct areas characterizing different tissue types.