Abstract. Diffusion MRI uses a multi-step data processing pipeline. With certain steps being prone to instabilities, the pipeline relies on considerable amounts of partly redundant input data, which requires long acquisition time. This leads to high scan costs and makes advanced diffusion models such as diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) inapplicable for children and adults who are uncooperative, uncomfortable or unwell. We demonstrate how deep learning, a group of algorithms in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This method allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models.

A 1 min multicontrast brain MRI scan based on EPI readouts has been presented in this feasibility study. Preliminary data show potential for clinical brain MRI use with minimal bore time for the patient. Such short examination time could be useful (e.g., for screening and acute stroke). The sequence may also help planning conventional brain MRI scans if run at the beginning of an examination. Magn Reson Med 79:3045-3054, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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Purpose:We investigate the importance of high gradient-amplitude and high slew-rate on oscillating gradient spin echo (OGSE) diffusion imaging for human brain imaging and evaluate human brain imaging with OGSE on the MAGNUS head-gradient insert (200 mT/m amplitude and 500 T/m/s slew rate). Methods: Simulations with cosine-modulated and trapezoidal-cosine OGSE at various gradient amplitudes and slew rates were performed. Six healthy subjects were imaged with the MAGNUS gradient at 3T with OGSE at frequencies up to 100 Hz and b = 450 s/mm 2 . Comparisons were made against standard pulsed gradient spin echo (PGSE) diffusion in vivo and in an isotropic diffusion phantom. Results: Simulations show that to achieve high frequency and b-value simultaneously for OGSE, high gradient amplitude, high slew rates, and high peripheral nerve stimulation limits are required. A strong linear trend for increased diffusivity (mean: 8-19%, radial: 9-27%, parallel: 8-15%) was observed in normal white matter with OGSE (20 Hz to 100 Hz) as compared to PGSE. Linear fitting to frequency provided excellent correlation, and using a short-range disorder model provided radial longterm diffusivities of D ∞,MD = 911 ± 72 µm 2 /s, D ∞,PD = 1519 ± 164 µm 2 /s, and D ∞,RD = 640 ± 111 µm 2 /s and correlation lengths of l c,MD = 0.802 ± 0.156 µm, l c,PD = 0.837 ± 0.172 µm, and l c,RD = 0.780 ± 0.174 µm. Diffusivity changes with OGSE frequency were negligible in the phantom, as expected. Conclusion: The high gradient amplitude, high slew rate, and high peripheral nerve stimulation thresholds of the MAGNUS head-gradient enables OGSE acquisition for in vivo human brain imaging. K E Y W O R D S diffusion imaging, head-gradient, microstructure | 951 TAN eT Al.