Optimization and numerical evaluation of multi-compartment diffusion MRI using the spherical mean technique for practical multiple sclerosis imaging

Sean P Devan; Xiaoyu Jiang; Francesca Bagnato; Junzhong Xu

doi:10.1016/j.mri.2020.09.002

Optimization and numerical evaluation of multi-compartment diffusion MRI using the spherical mean technique for practical multiple sclerosis imaging

Magn Reson Imaging. 2020 Dec:74:56-63. doi: 10.1016/j.mri.2020.09.002. Epub 2020 Sep 6.

Authors

Sean P Devan¹, Xiaoyu Jiang², Francesca Bagnato³, Junzhong Xu⁴

Affiliations

¹ Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
² Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
³ Neuroimaging Unit/Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Multiple Sclerosis Center, Neurology Department, TN Health Valley Veteran Affairs, Nashville, TN 37232, USA.
⁴ Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA. Electronic address: junzhong.xu@vanderbilt.edu.

Abstract

Background: The multi-compartment diffusion MRI using the spherical mean technique (SMT) has been suggested to enhance the pathological specificity to tissue injury in multiple sclerosis (MS) imaging, but its accuracy and precision have not been comprehensively evaluated.

Methods: A Cramer-Rao Lower Bound method was used to optimize an SMT protocol for MS imaging. Finite difference computer simulations of spins in packed cylinders were then performed to evaluate the influences of five realistic pathological features in MS lesions: axon diameter, axon density, free water fraction, axonal crossing, dispersion, and undulation.

Results: SMT derived metrics can be biased by some confounds of pathological variations, such as axon size and free water fraction. However, SMT in general provides valuable information to characterize pathological features in MS lesions with a clinically feasible protocol.

Conclusion: SMT may be used as a practical MS imaging method and should be further improved in clinical MS imaging.

Keywords: Axon; Diffusion; MRI; MS; Microstructure; SMT.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Axons / pathology
Diffusion Magnetic Resonance Imaging / methods*
Humans
Image Processing, Computer-Assisted
Multiple Sclerosis / diagnostic imaging*
Multiple Sclerosis / pathology

Abstract

Publication types

MeSH terms

Grants and funding