Simultaneous Acquisition of Diffusion Tensor and Dynamic Diffusion MRI

Abstract

Background: Dynamic diffusion magnetic resonance imaging (ddMRI) metrics can assess transient microstructural alterations in tissue diffusivity but requires additional scan time hindering its clinical application.

Purpose: To determine whether a diffusion gradient table can simultaneously acquire data to estimate dynamic and diffusion tensor imaging (DTI) metrics.

Study type: Prospective.

Subjects: Seven healthy subjects, 39 epilepsy patients (15 female, 31 male, age ± 15).

Field strength/sequence: Two-dimensional diffusion MRI (b = 1000 s/mm² ) at a field strength of 3 T. Sessions in healthy subjects-standard ddMRI (30 directions), standard DTI (15 and 30 directions), and nested cubes scans (15 and 30 directions). Sessions in epilepsy patients-two 30 direction (standard ddMRI, 10 nested cubes) or two 15 direction scans (standard DTI, 5 nested cubes).

Assessment: Fifteen direction DTI was repeated twice for within-session test-retest measurements in healthy subjects. Bland-Altman analysis computed bias and limits of agreement for DTI metrics using test-retest scans and standard 15 direction vs. 5 nested cubes scans. Intraclass correlation (ICC) analysis compared tensor metrics between 15 direction DTI scans (standard vs. 5 nested cubes) and the coefficients of variation (CoV) of trace and apparent diffusion coefficient (ADC) between 30 direction ddMRI scans (standard vs. 10 nested cubes).

Statistical tests: Bland-Altman and ICC analysis using a P-value of 0.05 for statistical significance.

Results: Correlations of mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were strong and significant in gray (ICC > 0.95) and white matter (ICC > 0.95) between standard vs. nested cubes DTI acquisitions. Correlation of white matter fractional anisotropy was also strong (ICC > 0.95) and significant. ICCs of the CoV of dynamic ADC measured using repeated cubes and nested cubes acquisitions were modest (ICC >0.60), but significant in gray matter.

Conclusion: A nested cubes diffusion gradient table produces tensor-based and dynamic diffusion measurements in a single acquisition.

Level of evidence: 2 TECHNICAL EFFICACY: Stage 1.

Keywords: acquisition; brain; diffusion tensor imaging; dynamic diffusion; gradient orientation; neuroimaging.

Figure 1.. Derivation and arrangement of nested cubes diffusion tensor imaging vector sets.

Five distinct triplets of mutually orthogonal vectors were computed using a geometric approach to nest five cubes in a regular dodecahedron (top row). The 15 distinct directions corresponding to the corners of each cube provide uniform coverage over a spherical shell. A numerical solution was used to distribute thirty directions across a spherical shell. This solution was constrained by the condition that the vector set should consist only of triplets of mutually orthogonal vectors. For each vector configuration, diffusion weighting was applied sequentially across each triplet of mutually orthogonal directions. This allows for simultaneous acquisition of data useful for tensor estimation for diffusion tensor imaging (DTI) and a time series of measurements of apparent diffusion coefficient (ADC).

Figure 2.. Bland-Altman subsampled plots of diffusivity metrics between two acquisitions within a single scan session.

Bland-Altman scatterplots of voxel-wise differences versus the means of mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) between two scans were computed for intracranial voxels within the skull stripped brain masks across seven subjects. Each datapoint corresponds to a voxel compared between two acquisitions. Due to the density of the voxels (>0.9 × 10⁶ voxels being compared), every 2500^th) pair was plotted as a datapoint. The bias (gray line) with limits of agreement (grey dashed lines), were defined as the median with lower and upper 2.5^th percentiles respectively of the percent change in each diffusivity metric, and were computed using all masked intracranial voxels.

Figure 3.. Comparison of root mean square error of tensor-based diffusivity metrics using standard and five nested cubes diffusion tensor imaging gradient tables.

Tensor metrics were compared between two repeated standard 15 direction DTI acquisitions (column four) and standard 15 direction and five nested cubes DTI acquisitions (right column). Both comparison show that the majority of voxels have low root mean squared error (RMSE), with the exception bein regions contaminated by cerebrospinal fluid (CSF). The majority of white matter voxels show low RMSE of fractional anisotropy (FA) and low angular error (θ) of fiber orientation measured between acquisitions (ε₁, ε₂).

Figure 4.. Coefficient of variation of dynamic trace and apparent diffusion coefficient.

Coefficients of variation (CoV) of the dynamic trace (top row) and apparent diffusion coefficient (ADC) (bottom row) were computed on a voxel-wise basis from acquisitions using ten repeated cubes (left column), ten nested cubes (middle column) and a standard 30 direction diffusion tensor imaging (DTI) gradient table consisting of non-orthogonal triplets of diffusion weighted directions (right column).

Figure 5.. Probability distributions of temporal signal-to-noise ratio of trace and apparent diffusion coefficient in gray matter and white matter measured using ten repeated cubes and ten nested cubes gradient tables.

Temporal signal-to-noise ratio (tSNR) was computed on a voxel wise basis in white matter (top row) and gray matter (bottom row) across 24 subjects (seven healthy volunteers, 17 patients under evaluation for epilepsy) using data acquired with ten repeated cubes and ten nested cubes gradient tables. The tSNR of temporal trace (left column) and ADC (right column) have highly overlapping probability distribution functions (pdf). tSNR, computed by dividing the temporal mean by the temporal standard deviation and is therefore proportionate to the inverse of the coefficient of variation. To remain consistent with the transformations applied to the CoV data used for statistical analysis, the tSNR is shown on a natural log scale with arbitrary units. The medians of each pdf are shown with dashed lines, along with the numerical values for each acquisition method (blue – 10 repeated cubes, red – 10 nested cubes).