Gastao Cruz^{1}, Andreia S. Gaspar^{1}, Tom Bruijnen^{2}, RenĂ© Botnar^{1}, and Claudia Prieto^{1}

Magnetic Resonance Fingerprinting estimates multi-parametric maps from a series of highly undersampled time-point images. However, MRF scan times are still long due to the large amount of time-point images (~1000) required to produce accurate multi-parametric maps. Here we propose to exploit redundant information in time-point images with similar contrast to accelerate the MRF scan by further undersampling each time-point image and/or significantly reducing the number of required images in the series. The proposed approach achieved an acceleration factor of 5.7× compared to conventional undersampled MRF while maintaining parametric map quality.

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Figure 1: **a)** Gridding
reconstruction used in conventional MRF at different time points *t* in the acquisition (8 spokes per time-point). **b)** MRF-SOHO reconstruction at different
time points *t* in the acquisition (8 spokes per time-point). Diagnostic
quality images are reconstructed at each time point with MRF-SOHO without
affecting the contrast of the time-point images.** c)** Flip angle pattern used presents smooth evolution. Coloured time-points
correspond to the images in **a)** and **b)**. **d)** Soft-weights
(*W*_{t}^{n})
for different neighbouring time-points
(n)
as a function of k-space radius *(k*_{r}). The
further away the time-point, the less low frequency data is shared.

Figure 2: T_{1} and T_{2} measurement plots (in ms) with conventional MRF and
MRF-SOHO reconstructions, using 8 spokes, 1000 time points; 4 spokes, 500 time
points; and 2 spokes, 500 time points. An estimation bias of high T_{1}
values was observed with the conventional MRF reconstruction in comparison with
the ground-truth phantom values. MRF-SOHO correctly estimated T_{1},
even at increased undersampling factors and reduced number of time points (4
spokes, 500 time points and 2 spokes, 500 time points). MRF-SOHO produced more
accurate and precise measurements than conventional MRF, particularly when less
data (time points and/or radial spokes) was used.

Figure 3: T_{1} and T_{2} maps (in ms) for conventional MRF
reconstruction and the proposed MRF-SOHO reconstruction using 4 radial spokes and different
number of time points (1000, 500, 350 and 100). The conventional MRF
reconstruction produces good quality parametric maps with 1000 and 500 time
points, but starts failing with reduced number of time-point images. The proposed
MRF-SOHO reconstruction produces good quality and accurate parametric maps in
all cases, however the signal-to-noise ratio is reduced in cases of high
acceleration.

Figure 4: T_{1} and T_{2} maps (in ms) for conventional MRF
reconstruction and the proposed MRF-SOHO reconstruction using 500 time points
and different number of radial spokes (8, 4 and 2). The conventional MRF
reconstruction produces good quality parametric maps with 8 radial spokes, but
quickly deteriorates with increased undersampling factor per time-point image.
The proposed MRF-SOHO reconstruction produces good quality and accurate
parametric maps in all cases, however the signal-to-noise ratio is reduced in
cases of high acceleration.

Figure 5: T_{1} (top) and T_{2} (bottom) maps (in ms) for the conventional MRF (left)
reconstruction using 1000 time points, 8 spokes and the proposed
MRF-SOHO (right) using 350 time points, 4 spokes (~5.7× accelerated
relative to conventional MRF). Literature T_{1} and T_{2}
values are show in parenthesis. The conventional MRF reconstruction produces
overall accurate parametric maps, however a slight underestimation of T_{1}
for white and grey matter is observed. The proposed MRF-SOHO achieved accurate
estimation of T_{1} and T_{2} for all tissues despite the high
acceleration factor, however a slight overall loss in signal-to-noise ratio was
also observed.