Chia-Ling Chang^{1}, Jr-Yuan George Chiou^{2}, Ming-Long Wu^{1,3,4}, Shang-Yueh Tsai^{5}, Stephan Ernst Maier^{2,6}, Bruno Madore^{2}, and Tzu-Cheng Chao^{1,4}

A novel technique, Fast Diffusion Imaging with High Angular Resolution, is proposed to achieve whole-brain HARDI scans for clinical applications with better geometrical fidelity and shorter scan time. The present study compares tractography results and diffusion properties of each analyzed fiber tract among four-fold segmented (multi-shot) HARDI scans with different acceleration rates and a clinically used sequence with two-fold SENSE. A fully sampled four-shot HARDI scan was used as the reference. The results suggest that the novel acceleration strategy permits a four-minute scan with fairly compatible results while the clinically used method takes ten minutes.

1. Chao, T.C., et al., Fast diffusion imaging with high angular resolution. Magn Reson Med, 2016.

2. Chen, N.K., et al., A robust multi-shot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE). Neuroimage, 2013. 72: p. 41-7.

3. Landman, B.A., et al., Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI. Neuroimage, 2012. 59(3): p. 2175-86.

4. Garyfallidis, E., et al., Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform, 2014. 8: p. 8.

5. Ashburner, J., A fast diffeomorphic image registration algorithm. Neuroimage, 2007. 38(1): p. 95-113.

6. Zimmerman-Moreno, G., et al., Whole brain fiber-based comparison (FBC)-A tool for diffusion tensor imaging-based cohort studies. Hum Brain Mapp, 2016. 37(2): p. 477-90.

Table 1. The table summarizes the sequence parameters in this study. The total scan times for the reduced number of diffusion encoding
directions is extrapolated based on the accelerated sampling scheme and the
actual TR.

Fig. 1.
(a) The Uncinate
Fasciculus reconstruction from the accelerated dataset is shown. The red region indicates the
overlap volume between reference and accelerated dataset. (b) The
diffusion indices including MD, FA and GFA within the overlapping volume were compared for the assessment of correlation and prediction interval. (c) The figure shows
how the morphological distance is calculated based on the resampling of the
tracts into 20 points, taking the central 10 as the main backbone and the other
two sides as the tail portion.

Fig. 2. The statistics depicts the percentage of overlapping volume fraction among the tested protocols. Regardless of acceleration factor,
the overlapping volume fractions are all over 80% for CC. Even in the IFO, the overlapping tract volume equals 60% on average provided that more than 32
diffusion encoding directions are used.
In comparison with the clinical protocol, the accelerated 1SHOT-R4-D128 and
1SHOT-R4-D64 acquisitions have comparable performance.

Fig. 3. All sub-figures
show that as the number of diffusion encoding
directions decreases, the correlation coefficient
decreases and the prediction interval increases. Due to differences in distortion, the pixel-wise
correspondence correlation of 1SHOT-R2-D128 has lower values, despite its higher overlapping tract volume with the
fully sampled data.

Fig. 4. The averaged
morphological distance of tracked fibers around the tails
are generally higher than those within the center region. As the acceleration
rate increases, the values also increases leading to high deviation from the
corresponding fiber tracts. Judging from the result, the median of the averaged
morphological distance is smaller than 3 voxels for the central region for the
reconstructions of 1SHOT-R4-D42, D64 and D128 and the clinical protocol
1SHOT-R2-D128.