Jana Hutter^{1}, Markus Nilsson^{2}, Daan Christiaens^{1}, Torben Schneider^{3}, Anthony N Price^{1}, Joseph V Hajnal^{1}, and Filip Szczepankiewicz^{2,4}

Diffusion encoding along multiple directions in a single shot facilitates probing of tissue microstructure that is not accessible with conventional (linear) tensor encoding. However, it tends to engage gradients on multiple axes in a pattern that yields higher energy consumption, which can become a critical limiting factor for gradient system performance. Here, we show that Slice Interleaved Free-waveform Imaging (SIFI) of b-tensor size, orientation, and shape reduces peak power consumption and heating, which translates to markedly reduced repetition time, shorter examination times and higher temporal SNR.

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[2] Westin, C.-F., Knutsson, H., Pasternak, O., Szczepankiewicz, F., Ozarslan, E., van Westen, D., et al. (2016). Q-space trajectory imaging for multidimensional diffusion MRI of the human brain. NeuroImage, 135(C), 345–362. http://doi.org/10.1016/j.neuroimage.2016.02.039

[3] Eriksson, S., Lasič, S., & Topgaard, D. (2013). Isotropic diffusion weighting in PGSE NMR by magic-angle spinning of the q-vector. Journal of Magnetic Resonance (San Diego, Calif : 1997), 226, 13–18. http://doi.org/10.1016/j.jmr.2012.10.015

[4] Lasič, S., Szczepankiewicz, F., Eriksson, S., Nilsson, M. & Topgaard, D. 2014. Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector. Frontiers in Physics, 2, 11

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[6] Szczepankiewicz, F., Sjölund, J., Ståhlberg, F., Lätt, J. & Nilsson, M. 2016. Whole-brain diffusional variance decomposition (DIVIDE): Demonstration of technical feasibility at clinical MRI systems. arXiv:1612.06741

[7] Szczepankiewicz, F. and Nilsson, M. 2018. Maxwell-compensated waveform design for asymmetric diffusion encoding. Submitted to Int. Soc. Magn. Reson. Med. Paris, France.

[8] Hutter, J., Christiaens, D., Kuklisova-Murgasova, M., Cordero-Grande, L., Slator, P., Price, A., Rutherford, M., Hajnal, J. V , “Dynamic field mapping and motion correction using interleaved double spin-echo diffusion MRI” 1 Oct 2017, Lecture Notes in Computer Science - MICCAI 2017, Vol. 10433 LNCS, 523-531

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[10] Hutter J, Tournier, D., Price, A., Cordero-Grande, L., Hughes, EJ., Malik, S., Steinweg, J., Bastiani, M., Sotiropoulos, S., Andersson, J., Edwards, D., Hajnal, JV, “Time-efficient and flexible design of optimized multishell HARDI diffusion”, Magnetic Resonance in Medicine 2017, in-press

[11] Hua Wu, Qiyuan Tian, Christian Poetter, Kangrong Zhu, Matthew J Middione, Adam B Kerr, Jennifer A McNab, and Robert F Doughert, Whole Brain Inversion Recovery Diffusion Weighted Imaging Using Slice-Shuffled Acquisition ISMRM 2017, 0387

Fig.1:
Schematic illustration of the scan-interleaved, volume-interleaved and
slice-interleaved versions. Thereby only the size and the shape of
the encoding are illustrated by color, the orientations were omitted
for simplicity in this representation.

Fig.2:
Diffusion encoding gradient waveforms that were used for all acquired
datasets and simulations. The two periods, separated by zero
gradients, are waveforms played out before and after the refocusing
pulse. Spherical and linear b-tensor encoding (STE and LTE) are shown
in the left and right panels, respectively.

Fig.3:
Thermal load evolution over slices for volume-sorted,
volume-interleaved and slice-interleaved cases.

Fig.
4:
The
first block of an SIFI dataset acquired with the described protocol
before and after sorting (top and bottom rows) in coronal and
transverse view.