Ouri Cohen^{1}

Recently, MR fingerprinting (MRF) has been proposed as a means of disentangling simultaneously excited slices by exciting each slice with a distinct acquisition schedule. A notable drawback of this approach, which is particularly acute for multi-parametric dictionaries, is the linear increase in reconstruction time with the number of slices and the potential reduction in accuracy. Here we describe an extension to our previously described MRF-DRONE method that can overcome these issues. Our method can enable larger acceleration factors and faster reconstruction of multi-parametric data.

[1] M. Barth, F. Breuer, P. J. Koopmans, D. G. Norris, and B. A. Poser, “Simultaneous multislice (SMS) imaging techniques,” Magn. Reson. Med., vol. 75, no. 1, pp. 63–81, 2016.

[2] Y. Jiang et al., “Use of pattern recognition for unaliasing simultaneously acquired slices in simultaneous multislice MR fingerprinting,” Magn. Reson. Med., 2016.

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

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

[5] M. Abadi et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” ArXiv Prepr. ArXiv160304467, 2016.

[6] 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.

[7] O. Cohen, B. Zhu, and M. S. Rosen, “Characterization of Sparsely Trained Deep Learning Reconstruction of Noisy MR Fingerprinting Data,” in Proceedings of the International Society of Magnetic Resonance in Medicine, Paris, France, 2018.

[8] O. Cohen, C. T. Farrar, B. Zhu, and M. S. Rosen, “Fast Deep Learning Reconstruction of Highly Multi-dimensional MR Fingerprinting Data,” presented at the ISMRM Workshop on Machine Learning, Pacific Grove, CA, 2018.

Figure 1: The MRF-EPI SMS pulse sequence. Each RF pulse simultaneously
excited 2 slices, labeled ‘a’ and ‘b’, with different, randomly selected, flip
angles. The TR was the same for both slices but varied for each excitation according
to the random acquisition schedule.

Figure 2: Illustration
of the SMS-DRONE framework. The signal obtained from the simultaneous excitation
of multiple slices is fed voxel-wise to a trained neural network which outputs
the corresponding tissue parameters (T1,T2) of each slice.

Figure 3: Reconstructed quantitative T1 and T2 tissue maps obtained with
SMS-DRONE in comparison to the true values. A percent error map is shown for
each slice along with the mean error for each slice. Note the good agreement
between the SMS-DRONE reconstruction and the true maps.