Summary: MR fingerprinting which has the potential to identify heart disease, cancers, multiple sclerosis, and other conditions by examining various human tissues at very early stages of disease.
March 22, 2013 - In a recent study,1 researchers
introduced an approach to data acquisition, postprocessing and visualization called
resonance fingerprinting’ (MRF), which
has the potential identify heart disease, cancers, multiple sclerosis, and
other conditions by examining various human tissues at very early stages of
To date, MR imaging has been mainly restricted to
the qualitative probing of only a limited set of the properties that can in
principle be accessed by this technique. MRF permits the simultaneous,
noninvasive quantification of multiple important properties of a material or
tissue, providing an alternative way to quantitatively detect and analyze
complex changes that can represent physical alterations of a substance or early
indicators of disease.
According to the researchers, 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 an MR study, and
potentially lead to new diagnostic testing methodologies. When paired with an
appropriate pattern-recognition algorithm, MRF suppresses measurement errors
and can improve measurement accuracy.
The MRF concept 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.
The ability to analyze 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.
All of the data were acquired on a 1.5-T whole body scanner
(Espree, Siemens Healthcare) with a standard 32-channel head receiver coil.
Images from each acquisition block were reconstructed separately using
non-uniform Fourier transform. The resultant time series of images was used to
determine the value for the
parameters (T1, T2, M0, and off-resonance). 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, T1,
T2 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.
The researchers concluded that 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.
1. Ma D, Gulani V, Seiberlich N, et al. Magnetic
resonance fingerprinting. Nature. 2013495:187–192. doi:10.1038/nature11971
For more information: www.siemens.com/healthcare and www.appliedradiology.com and www.appliedradiationoncology.com