Journal
FRONTIERS IN NEUROSCIENCE
Volume 12, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2018.00102
Keywords
diffusion magnetic resonance imaging; brain; white matter; computer-assisted image processing; theoretical models
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Funding
- Zhejiang Provincial Natural Science Foundation of China [LY13H180006]
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Purpose: To present a new modified tri-exponential model for diffusion weighted imaging (DWI) to detect the strictly diffusion-limited compartment, and to compare it with the conventional bi- and tri-exponential models. Methods: Multi-b-value diffusion-weighted imaging (DWI) with 17 b-values up to 8,000 s/mm(2) were performed on six volunteers. The corrected Akaike information criterions (AICc) and squared predicted errors (SPE) were calculated to compare these three models. Results: The mean f(0) values were ranging 11.9-18.7% in white matter ROIs and 1.2-2.7% in gray matter ROIs. In all white matter ROIs: the AICcs of the modified tri-exponential model were the lowest (p < 0.05 for five ROIs), indicating the new model has the best fit among these models; the SPEs of the bi-exponential model were the highest (p 0.05), suggesting the bi-exponential model is unable to predict the signal intensity at ultra-high b-value. The mean ADC(very-slow) values were extremely low in white matter (1-7 x 10(-6) mm(2)/s), but not in gray matter (251-445 x 10(-6) mm(2)/s), indicating that the conventional tri-exponential model fails to represent a special compartment. Conclusions: The strictly diffusion-limited compartment may be an important component in white matter. The new model fits better than the other two models, and may provide additional information.
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