White matter biomarkers from fast protocols using axially symmetric diffusion kurtosis imaging

Abstract

White matter tract integrity (WMTI) can characterize brain microstructure in areas with highly aligned fiber bundles. Several WMTI biomarkers have now been validated against microscopy and provided promising results in studies of brain development and aging, as well as in a number of brain disorders. Currently, WMTI is mostly used in dedicated animal studies and clinical studies of slowly progressing diseases, and has not yet emerged as a routine clinical tool. To this end, a less data intensive experimental method would be beneficial by enabling high resolution validation studies, and ease clinical applications by speeding up data acquisition compared with typical diffusion kurtosis imaging (DKI) protocols utilized as part of WMTI imaging. Here, we evaluate WMTI based on recently introduced axially symmetric DKI, which has lower data demand than conventional DKI. We compare WMTI parameters derived from conventional DKI with those calculated analytically from axially symmetric DKI. We employ numerical simulations, as well as data from fixed rat spinal cord (one sample) and in vivo human (three subjects) and rat brain (four animals). Our analysis shows that analytical WMTI based on axially symmetric DKI with sparse data sets (19 images) produces WMTI metrics that correlate strongly with estimates based on traditional DKI data sets (60 images or more). We demonstrate the preclinical potential of the proposed WMTI technique in in vivo rat brain (300 μm isotropic resolution with whole brain coverage in a 1 h acquisition). WMTI parameter estimates are subject to a duality leading to two solution branches dependent on a sign choice, which is currently debated. Results from both of these branches are presented and discussed throughout our analysis. The proposed fast WMTI approach may be useful for preclinical research and e.g. clinical evaluation of patients with traumatic white matter injuries or symptoms of neurovascular or neuroinflammatory disorders.

Keywords: MRI; WMTI; biophysics; diffusion; kurtosis; white matter.

Copyright © 2017 John Wiley & Sons, Ltd.

Figures

Fig. 1

Results from numerical simulations comparing performance of WMTI to aWMTI. Histograms show relative errors compared to ground truth values for each method. The red line marks zero error. Text inside each plot reports median relative error and mid-95% range for each method.

Fig. 2

Comparison of parameter estimates from WMTI and aWMTI in rat SC white matter.

Fig. 3

A) Mapping of the average intra-voxel fiber dispersion (in degrees) in the rat SC. The red outline shows the WM region analyzed throughout. B) Histogram of the average intra-voxel fiber dispersion mapped in panel A.

Fig. 4

Scatterplots comparing WMTI and aWMTI estimates in both branches in whole brain (4401 WM pixels) of one human subject. On average less than 9% of the data fall outside the shown ranges.

Fig. 5

Data example showing estimates of three faWMTI parameters (Branch 1) based on a 1-9-9 acquisition in live rat brain at an isotropic resolution of 300 μm. Axial and coronal slice planes are shown. The parameter values are overlaid on the b=0 images from the 1-9-9 data set.

Fig. 6

Histograms of WMTI estimates of De,|| and Da for Branch 1 (top row) and Branch 2 (bottom row) in rat SC (column A), human brain (column B). Column C shows faWMTI estimates from in vivo rat brain. Columns B and C show data from all subjects/animals. Vertical red lines mark the free water diffusivity at the sample temperature.

Fig. 7

Correlation between θC (in degrees) and WMTI estimates of Da and De,|| for both branches in rat SC WM. The black line shows a robust fit (function ‘robustfit’ in Matlab) to the data.

Fig. 8

aWMTI branch behavior in rat SC (A), human brain (B). Vertical red lines mark the free water diffusivity at the sample temperature. Panel C shows correlations between θC (in degrees) and aWMTI estimates of Da and De,|| for both branches in rat SC WM. The black line shows a robust fit to the data.