Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging

Abstract

Q-ball imaging (QBI) is a popular high angular resolution diffusion imaging (HARDI) technique used to study brain architecture in vivo. Simulation and phantom-based studies suggest that QBI results are affected by the b-value, the number of diffusion weighting directions, and the signal-to-noise ratio (SNR). However, optimal acquisition schemes for QBI in clinical settings are largely undetermined given empirical (observed) imaging considerations. In this study, we acquire a HARDI dataset at five b-values with 11 repetitions on a single subject to investigate the effects of acquisition scheme and subsequent analysis models on the accuracy and precision of measures of tissue composition and fiber orientation derived from clinically feasible QBI at 3T. Clinical feasibility entails short scan protocols - less than five minutes in the current study - resulting in lower SNR, lower b-values, and fewer diffusion directions than are typical in most QBI protocols with research applications, where time constraints are less prevalent. In agreement with previous studies, we find that the b-value and number of diffusion directions impact the magnitude and variation of QBI indices in both white matter and gray matter regions; however, QBI indices are most heavily dependent on the maximum order of the spherical harmonic (SH) series used to represent the diffusion orientation distribution function (ODF). Specifically, to ensure numerical stability and reduce the occurrence of false peaks and inflated anisotropy, we recommend oversampling by at least 8–12 more diffusion directions than the number of estimated coefficients for a given SH order. In addition, in an equal scan time comparison of QBI accuracy, we find that increasing the directional resolution of the HARDI dataset is preferable to repeating observations; however, our results indicate that as few as 32 directions at a low b-value (1,000 s/mm2) captures most of the angular information in the q-ball ODF. Our findings provide guidance for determining an optimal acquisition scheme for QBI in the low SNR and low scan time regime, and suggest that care must be taken when choosing the basis functions used to represent the QBI ODF.

Histological validation of diffusion MRI fiber orientation distributions and dispersion

“…Further, we find that some measures of quality (i.e. JSD and FP peaks) are largely influenced by the fit SH order used to represent that function on a sphere, an effect observed in previous reproducibility studies (Schilling et al, 2017c).…”

“…The ACC, like the linear correlation coefficient, measures the degree to which two functions over a sphere are correlated and can be calculated directly from the spherical harmonic coefficients of the two functions (Anderson, 2005). The JSD measures the distance between two probability distributions (in this case, over a sphere) and has been used in the dMRI literature to compare ODFs from different reconstruction methods (Chiang et al, 2008), and to quantify reproducibility of dMRI algorithms (Cohen-Adad et al, 2011; Schilling et al, 2017c). The JSD is bounded between 0 and 1, where lower values indicate greater similarity of distributions.…”

Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge

“…These methods are commonly referred to as high angular resolution diffusion imaging (HARDI), eg, q‐ball, constrained spherical deconvolution (CSD), persistent angular structure (PAS) . HARDI methods enable characterization of more than a single fiber direction per voxel, but have often been shown to be limited when more than two fiber populations exist per voxel . While there is a definite gain in sensitivity when using HARDI methods, there remain critical questions of their reproducibility …”

Empirical estimation of intravoxel structure with persistent angular structure and Q-ball models of diffusion weighted MRI

“…Previous results have revealed spherical harmonic order shows dependence on the number of gradient directions and b value. 32 Additionally, lower order harmonics lead to less angular resolution. High spherical harmonic orders and high b values yield decrease in angular error for high SNR sequences for b values up till 6000 s∕mm 2 .…”