Rasim Boyacioglu^{1}, Debra McGivney^{1}, Dan Ma^{1}, Yun Jiang^{1}, and Mark Griswold^{1}

Magnetic Resonance Fingerprinting (MRF) maps various tissue properties and system parameters simultaneously. MRF time series, which are matched to a precalculated dictionary, are often obtained with fast acquisition of low resolution images with undersampled spiral trajectories using a regular sampling pattern. In this work, we propose to order a set of spiral trajectories based on dictionary variance instead of the standard sequential or golden-angle ordering. Phantom and in vivo results show that the variance based optimized order converges faster to expected true values. The optimized order does not limit other MRF optimization approaches and can be applied to any MRF sequence.

1. Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature 2013;495: 187–192.

2. Russek SE, Boss M, et al. Characterization of NIST/ISMRM MRI system phantom. In Proceedings of the 20th Annual Meeting of ISMRM, Melbourne, Australia, 2012. Abstract 2456.

3. https://collaborate.nist.gov/mriphantoms/bin/view/MriPhantoms/MRISystemPhantom

4. Jiang Y, Ma D, Seiberlich N, et al. MR Fingerprinting Using Fast Imaging with Steady State Precession (FISP) with Spiral Readout. Magn Reson Med 2015;74:1621-1631.

Figure
1. Flip angle (a) and TR (b) pattern for FISP MRF sequence. In (c) dictionary
variance along the tissue dimension is plotted for all time points. It is
evident that time points with higher variance contribute more for
differentiation of dictionary entries and mapping true values with dictionary
matching.

Figure
2. Phantom T1
results when matching is done with various number of time points. N500 with
optimized order maps T1 with limited artifacts. Across all reconstructions,
optimized order would require fewer time points for similar image quality.

Figure 3. Phantom T2 results when matching is
done with various number of time points. T1 converges faster than T2. With the
same number of time points optimized order generates maps which are closer to
gold standard of N2000.

Figure
4. In
vivo T1 results when matching is done with various number of time points. T1 in
vivo does not show any apparent benefits for optimized order. It appears that CSF
voxels need more time points to converge for both ordering schemes.

Figure
5. In vivo T2 results when matching is done with various number of time points.
Various regions around CSF (pointed with arrows) with long T2 benefit from the optimized
order.