Effects of b-value and number of gradient directions on diffusion MRI measures obtained with Q-ball imaging
Presentation + Paper
24 February 2017 Effects of b-value and number of gradient directions on diffusion MRI measures obtained with Q-ball imaging
Author Affiliations +
Abstract
High-angular-resolution diffusion-weighted imaging (HARDI) MRI acquisitions have become common for use with higher order models of diffusion. Despite successes in resolving complex fiber configurations and probing microstructural properties of brain tissue, there is no common consensus on the optimal b-value and number of diffusion directions to use for these HARDI methods. While this question has been addressed by analysis of the diffusion-weighted signal directly, it is unclear how this translates to the information and metrics derived from the HARDI models themselves. Using a high angular resolution data set acquired at a range of b-values, and repeated 11 times on a single subject, we study how the b-value and number of diffusion directions impacts the reproducibility and precision of metrics derived from Q-ball imaging, a popular HARDI technique. We find that Q-ball metrics associated with tissue microstructure and white matter fiber orientation are sensitive to both the number of diffusion directions and the spherical harmonic representation of the Q-ball, and often are biased when under sampled. These results can advise researchers on appropriate acquisition and processing schemes, particularly when it comes to optimizing the number of diffusion directions needed for metrics derived from Q-ball imaging.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kurt G. Schilling, Vishwesh Nath, Justin Blaber, Robert L. Harrigan, Zhaohua Ding, Adam W. Anderson, and Bennett A. Landman "Effects of b-value and number of gradient directions on diffusion MRI measures obtained with Q-ball imaging", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330N (24 February 2017); https://doi.org/10.1117/12.2254545
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Cited by 7 scholarly publications.
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KEYWORDS
Diffusion

Diffusion tensor imaging

Gold

Tissues

Brain

Data acquisition

Diffusion magnetic resonance imaging

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