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Front. Neurosci. | doi: 10.3389/fnins.2018.00102

A modified tri-exponential model for multi-b-value diffusion-weighted imaging: a method to detect the strictly diffusion-limited compartment in brain

 Qiang Zeng1,  Feina Shi2,  Jianmin Zhang1, Chenhan Ling1, Fei Dong3 and  Biao Jiang3*
  • 1Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, China
  • 2Department of Neurology, Second Affiliated Hospital of Zhejiang University School of Medicine, China
  • 3Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, China

Purpose: to present a new modified tri-exponential model for diffusion-weighted imaging 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 8000 s/mm2 were performed on 6 volunteers. The corrected Akaike information criterions (AICc) and squared predicted errors (SPE) were calculated to compare these three models. Results: The mean f0 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 ADCvery-slow values were extremely low in white matter (1 - 7×10-6 mm2/s), but not in gray matter (251-445×10-6 mm2/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.

Keywords: Diffusion Magnetic Resonance Imaging, Brain, white matter, Myelin Sheath, computer-assisted image processing

Received: 07 Nov 2017; Accepted: 12 Feb 2018.

Edited by:

Sune N. Jespersen, Aarhus University, Denmark

Reviewed by:

Lipeng Ning, Brigham and Women's Hospital, United States
Chantal M. Tax, Cardiff University, United Kingdom  

Copyright: © 2018 Zeng, Shi, Zhang, Ling, Dong and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Biao Jiang, Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Radiology, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China, jiangbiao@zju.edu.cn