Ouri Cohen^{1}

MR Fingerprinting schedule optimization can reduce scan times and improve accuracy but typically relies on minimization of indirect metrics rather than the actual reconstruction error due to the computational challenges involved in calculating the reconstruction error at each iteration of the optimization. Here we introduce a Deep Learning framework that can overcome these challenges and allow direct minimization of the reconstruction error. The proof-of-principle is demonstrated using simulations on a numerical brain phantom.

[1] O. Cohen and M. S. Rosen, “Algorithm comparison for schedule optimization in MR fingerprinting,” Magn. Reson. Imaging, 2017.

[2] B. Zhao, J. P. Haldar, K. Setsompop, and L. L. Wald, “Optimal experiment design for magnetic resonance fingerprinting,” in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, 2016, pp. 453–456.

[3] O. Cohen, B. Zhu, and M. S. Rosen, “MR fingerprinting deep reconstruction network (DRONE),” Magn. Reson. Med., 2018.

[4] M. Weigel, “Extended phase graphs: Dephasing, RF pulses, and echoes-pure and simple,” J. Magn. Reson. Imaging, vol. 41, no. 2, pp. 266–295, 2015.

[5] D. L. Collins et al., “Design and construction of a realistic digital brain phantom,” IEEE Trans. Med. Imaging, vol. 17, no. 3, pp. 463–468, 1998.

[6] O. Cohen, S. Huang, M. T. McMahon, M. S. Rosen, and C. T. Farrar, “Rapid and quantitative chemical exchange saturation transfer (CEST) imaging with magnetic resonance fingerprinting (MRF),” Magn. Reson. Med., 2017.

Figure 1: Overview of the proposed optimization scheme. For each
randomly selected schedule a dataset is generated and used to train a neural
network. A second dataset is used as test data for the trained network. The cost
is defined as the error between the true and reconstructed tissue values using
any suitable error metric.

Figure 2: The cost function network. The errors (cost) obtained
in the initial step are used to train a second network that maps between the
schedule space and the reconstruction error to allow an efficient search of
that space.

Figure 3: Initial (random) and optimized FA and TR schedules
obtained with the optimization network.

Figure 4: Percent error for the reconstructed T1 and
T2 maps as a function of SNR for the initial (blue) and optimized (red)
schedules. The optimized schedule resulted in lower error across all SNR levels
despite the shorter scan time and could be further reduced with improved
training.