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 5minutes 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 (1000s/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.

Figure 1

Standard pre-processing routines were performed within each session and within-scan normalization was performed. The effects of acquisition and model fitting were examined through subsampling the number of gradient directions used for the fits. Scan equivalent sessions were constructed to understand the tradeoffs between SNR and angular sampling.

Figure 2

QBI glyphs in crossing fiber (left) and single fiber (right) regions, for various b-values, number of gradient directions, and maximum SH order fit. Glyphs are shown min-max normalized and are displayed on top of fractional anisotropy maps.

Figure 3

The effect of the number of gradient directions, and maximum SH order on the GFA and Crossing Angle are shown in representative axial slices (A and B) and ROI-based analysis (C–E) for a b-value of 2,000 s/mm². Maximum SH order fit is indicated by line style (dotted line=4^th order; dash-dot=6^th order; dashed=8^th order; solid=10^th order). Note, in the Crossing Angle figures (B), voxels with only one resolved fiber population are shown as black. For results at all b-values, see Supplementary Figure 1.

Figure 4

The effect of the number of gradient directions, and maximum SH order on the log(TH) (A, B) and Number of Peaks (C) are shown as an ROI-based analysis for a b-value of 2,000 s/mm². Maximum SH order fit is indicated by line style (dotted line=4^th order; dash-dot=6^th order; dashed=8^th order; solid=10^th order). For results at all b-values, see Supplementary Figure 3.

Figure 5

The effect of b-value, number of gradient directions, and maximum SH order on JSD, AV1, and AV2 are presented in representative axial slices.

Figure 6

The effect of number of gradient directions, and maximum SH order on JSD, AV1, and AV2 are presented for an ROI-based analysis for a b-value of 2,000 s/mm². Maximum SH order fit is indicated by line style (dotted line=4^th order; dash-dot=6^th order; dashed=8^th order; solid=10^th order). For results at all b-values, see Supplementary Figure 4.

Figure 7

The effects of diffusion-weighting scheme on GFA and crossing angle for four scan-time-equivalent sessions are shown as representative axial slices and as ROI-based analysis. The three separate scan sessions are represented as triangles, diamonds, and circles. Horizontal lines indicate the mean over three sessions of all data acquired per session, for the corresponding STE, ROI, and b-value. Note that axis limits are different across ROIs (while the scale remains the same). Results are shown for a b-value of 2,000 s/mm², for results at all b-values, see Supplementary Figure 5. If no second fiber crossing is detected, no data point is shown.

Figure 8

The effects of diffusion-weighting scheme on Number of Peaks and log(TH) for four scan-time-equivalent sessions are shown as ROI-based analysis. The three separate scan sessions are represented as triangles, diamonds, and circles. Horizontal lines indicate the mean over three sessions of all data acquired per session, for the corresponding STE, ROI, and b-value. Note that axis limits are different across ROIs (while the scale remains the same). Results are shown for a b-value of 2,000 s/mm², for results at all b-values, see Supplementary Figure 6.

Figure 9

The effects of diffusion-weighting scheme on JSD, MAD1, and MAD2 for four scan-time-equivalent sessions shown as representative axial slices and as ROI-based analysis at a b-value of 2,000 s/mm². The three separate scan sessions are represented as triangles, diamonds, and circles. For ROI-based results at all b-values, see Supplementary Figure 7.