Optimization of data acquisition and analysis for fiber ball imaging

Hunter G Moss; Emilie T McKinnon; G Russell Glenn; Joseph A Helpern; Jens H Jensen

doi:10.1016/j.neuroimage.2019.07.005

Optimization of data acquisition and analysis for fiber ball imaging

Neuroimage. 2019 Oct 15:200:690-703. doi: 10.1016/j.neuroimage.2019.07.005. Epub 2019 Jul 5.

Authors

Hunter G Moss¹, Emilie T McKinnon², G Russell Glenn³, Joseph A Helpern⁴, Jens H Jensen⁵

Affiliations

¹ Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA.
² Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA; Department of Neurology, Medical University of South Carolina, Charleston, SC, USA.
³ Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA; Department of Neurology, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Department of Internal Medicine, Palmetto Health Richland Hospital, University of South Carolina School of Medicine, Columbia, SC, USA.
⁴ Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA; Department of Neurology, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
⁵ Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA. Electronic address: jense@musc.edu.

Abstract

The inverse Funk transform of high angular resolution diffusion imaging (HARDI) data provides an estimate for the fiber orientation density function (fODF) in white matter (WM). Since the inverse Funk transform is a straightforward linear transformation, this technique, referred to as fiber ball imaging (FBI), offers a practical means of calculating the fODF that avoids the need for a response function or nonlinear numerical fitting. Nevertheless, the accuracy of FBI depends on both the choice of b-value and the number of diffusion-encoding directions used to acquire the HARDI data. To inform the design of optimal scan protocols for its implementation, FBI predictions are investigated here with in vivo data from healthy adult volunteers acquired at 3 T for b-values spanning 1000 to 10,000 s/mm², for diffusion-encoding directions varying in number from 30 to 256 and for TE ranging from 90 to 120 ms. Our results suggest b-values above 4000 s/mm² with at least 64 diffusion-encoding directions are adequate to achieve reasonable accuracy with FBI for calculating axon-specific diffusion measures and for performing WM fiber tractography (WMFT).

Keywords: Axon; Diffusion MRI; Fiber ball imaging; Fiber orientation density function; Funk transform; White matter.

Publication types

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

MeSH terms

Adult
Axons*
Diffusion Magnetic Resonance Imaging / methods*
Humans
Models, Theoretical
Neuroimaging / methods*
White Matter / diagnostic imaging*

Abstract

Publication types

MeSH terms

Grants and funding