Min-xiong Zhou^{1}, Huiting Zhang^{2}, Yang Song^{3}, Guang Yang^{3}, and Xu Yan^{2}

^{1}Shanghai University of Medicine & Health Sciences, Shanghai, China, ^{2}MR Scientific Marketing, Siemens Healthcare, Shanghai, China, ^{3}Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univeristy, Shanghai, China

Advanced diffusion models such as NODDI, MAP-MRI are of high interests in brain research, but suffer from long acquisition time. Advanced under-sampling scheme were reported in previous studies for acceleration but are not commercially available. This study evaluates a simple and commercial available under-sampling scheme using the symmetric property of q-space, which could accelerate the acquisition by 2 fold. Results showed that it did not significant sacrifice the accuracy of quantitative maps. In addition, a symmetrically data copy step is needed to improve the estimation accuracy for both MAP-MRI and NODDI models.

Three q-space sampling schemes were compared in the study: 1) Full sampled data with all 98 directions, denotes as

To conclude, this study showed that the acquisition time of advanced diffusion models could be efficiently reduced by using half q-space sampling scheme in combination with symmetrically data copying to fill the other half data. Thus, this acceleration scheme could be applied widely in clinical routine or for special patient group such as children.

1. Ozarslan E, Koay CG, Shepherd TM, et al. Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure. Neuroimage, 2013;78:16-32.

2. Bernstein AS, Chen N, Trouard TP. Bootstrap analysis of diffusion tensor and mean apparent propagator parameters derived from multiband diffusion MRI. Magn Reson Med, 2019; 82:1796-1803.

3. Olson DV, Arpinar VE, Muftuler LT. Optimization of q-space sampling for mean apparent propagator MRI metrics using a genetic algorithm. NeuroImage, 2019; 199: 237-244.

4. Hutchinson EB, Avram AV, Irfanoglu MO, et al. Analysis of the effects of noise, DWI sampling, and value of assumed parameters in diffusion MRI models. Magn Reson Med, 2017; 78: 1767-1780.

5. Sangma Xie, Liangfu Chen, Nianming Zuo and Tianzi Jiang, DiffusionKit: A Light One-Stop Solution for Diffusion MRI Data Analysis, Journal of Neuroscience Methods, 2016; 273: 107-119.

Figure 1. ODI and ICVF maps (NODDI model) of
three different schemes (Full, Half and Half-to-Full) and their corresponding
percent of square coefficient of variation (CV) for a patient.

Figure 2. RTAP and NG maps (MAP-MRI model)
of three different schemes (Full, Half and Half-to-Full) and their
corresponding percent of square coefficient of variation (CV) for a patient.

Figure
3. Scatter plots of parameters from Full scheme against Half (Blue) and
Half-to-Full (Orange)on a voxel-by-voxel basis for the whole brain.