Matthew Lyon^{1}, Thomas Welton^{1}, Jerome Maller^{1,2}, MyungHo In^{3}, Ek Tsoon Tan^{4}, Matt Bernstein^{3}, Erin Gray^{3}, Yunhong Shu^{3}, John III Huston^{3}, and Stuart Grieve^{1,5}

We compared fibre tracking performance in the arcuate fasciculus across a range of angular resolutions, as well as a low distortion dataset using diffusion MRI data from a Compact 3T scanner with high-performance gradients. Tracking performance increased approximately linearly with greater angular resolution. Performance was also improved using a low-distortion diffusion sequence at a single relatively low angular resolution acquisition (33 directions).

One healthy adult was imaged using the GE Compact 3T MRI scanner (peak gradient amplitude 80 mT/m, slew rate 700 T/m/s) ^{3-5}. Frequency shifting ^{6} and gradient pre-emphasis ^{7} was applied to account for additional concomitant fields arising from the asymmetric transverse gradients. High-order gradient non-linearity correction with even-order terms was also employed ^{8}. The diffusion acquisitions were as follows:

- High spatial & angular resolution: “Multi-shell” 1.2 mm
^{3}, TE=58.6 ms, TR=6000 ms, FA=90^{°}, 750 diffusion-weighted volumes over 3 shells at b=700 (134), 1000 (214) and 2800 (402), plus 42 b=0 volumes, multiband factor=3, in-plane acceleration factor=2, ~80 minutes. This dataset was down-sampled to 700, 560, 420, 280, 140, 64 and 33 directions while retaining uniform angular distribution within shells. - Low Distortion: “MUSE”
^{9}1.2mm^{3}, TE=54.5 ms, TR=12500 ms, FA=90^{°}, 33 diffusion-weighted volume at b=1000 mm/s2 plus 1 b=0 volumes, in-plane acceleration factor=3, ~15 minutes.

T1-weighted images were acquired using MPRAGE PROMO: 1 mm^{3}, matrix=256x256 TE=2.4 ms, TR=5752 ms, TI=900 ms, FA=8^{°}.

Diffusion data were de-noised using the MRtrix3 package ^{10}. Susceptibility and eddy current induced distortions were then corrected using the FSL’s topup & eddy ^{11, 12}. Fibre orientation distributions were obtained by first estimating tissue response functions using the Dhollander algorithm, then applying a multi-shell multi-tissue constrained spherical deconvolution on the corrected dataset using the MRtrix3 package ^{13, 14}. Automated segmentation of the T1-weighted image was performed using Freesurfer ^{15}. The AF was tracked using a standardised approach (Figure 1) ^{2}.

- Tournier JD, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage. 2007;35(4):1459-72.
- Glasser MF, Rilling JK. DTI tractography of the human brain's language pathways. Cereb Cortex. 2008;18(11):2471-82.
- Foo TKF, Laskaris E, Vermilyea M, Xu M, Thompson P, Conte G, et al. Lightweight, compact, and high-performance 3T MR system for imaging the brain and extremities. Magn Reson Med. 2018;80(5):2232-45.
- Tan ET, Lee SK, Weavers PT, Graziani D, Piel JE, Shu Y, et al. High slew-rate head-only gradient for improving distortion in echo planar imaging: Preliminary experience. J Magn Reson Imaging. 2016;44(3):653-64.
- Weavers PT, Shu Y, Tao S, Huston J, 3rd, Lee SK, Graziani D, et al. Technical Note: Compact three-tesla magnetic resonance imager with high-performance gradients passes ACR image quality and acoustic noise tests. Med Phys. 2016;43(3):1259-64.
- Weavers PT, Tao S, Trzasko JD, Frigo LM, Shu Y, Frick MA, et al. B0 concomitant field compensation for MRI systems employing asymmetric transverse gradient coils. Magn Reson Med. 2018;79(3):1538-44.
- Tao S, Weavers PT, Trzasko JD, Shu Y, Huston J, 3rd, Lee SK, et al. Gradient pre-emphasis to counteract first-order concomitant fields on asymmetric MRI gradient systems. Magn Reson Med. 2017;77(6):2250-62.
- Tao S, Trzasko JD, Gunter JL, Weavers PT, Shu Y, Huston J, et al. Gradient nonlinearity calibration and correction for a compact, asymmetric magnetic resonance imaging gradient system. Phys Med Biol. 2017;62(2):N18-N31.
- Chen NK, Guidon A, Chang HC, Song AW. A robust multi-shot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE). Neuroimage. 2013;72:41-7.
- Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage. 2016;142:394-406.
- Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063-78.
- Andersson JL, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 2003;20(2):870-88.
- Dhollander T, Raffelt D, Connelly A. Accuracy of response function estimation algorithms for 3-tissue spherical deconvolution of diverse quality diffusion MRI data2018.
- Jeurissen B, Tournier J-D, Dhollander T, Connelly A, Sijbers J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage. 2014;103:411-26.
- Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage. 2012;61(4):1402-18.
- Tournier J-D, Calamante F, Connelly A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions2010.
- Smith RE, Tournier JD, Calamante F, Connelly A. Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage. 2012;62(3):1924-38.