Biophysical compartment models for single-shell diffusion MRI in the human brain: a model fitting comparison

• Published in: Physics in medicine & biology Vol. 67; no. 5; pp. 55009 - 55023
• Main Authors: Davis, Andrew D, Hassel, Stefanie, Arnott, Stephen R, Hall, Geoffrey B, Harris, Jacqueline K, Zamyadi, Mojdeh, Downar, Jonathan, Frey, Benicio N, Lam, Raymond W, Kennedy, Sidney H, Strother, Stephen C
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 07.03.2022
• Subjects: Anisotropy; Bayes Theorem; biophysical modelling; Brain - diagnostic imaging; compartment model; Diffusion Magnetic Resonance Imaging - methods; diffusion tensor imaging; diffusion-weighted imaging; DTI; Humans; Image Processing, Computer-Assisted - methods; magnetic resonance imaging; neuroimaging; White Matter - diagnostic imaging; magnetic resonance imaging; compartment model; diffusion-weighted imaging; DTI; biophysical modelling; diffusion tensor imaging; neuroimaging
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/ac46de

Clinically oriented studies commonly acquire diffusion MRI (dMRI) data with a single non-zero b-value (i.e. single-shell) and diffusion weighting of b = 1000 s mm−2. To produce microstructural parameter maps, the tensor model is usually used, despite known limitations. Although compartment models have demonstrated improved fits in multi-shell dMRI data, they are rarely used for single-shell parameter maps, where their effectiveness is unclear from the literature. Here, various compartment models combining isotropic balls and symmetric tensors were fitted to single-shell dMRI data to investigate model fitting optimization and extract the most information possible. Full testing was performed in 5 subjects, and 3 subjects with multi-shell data were included for comparison. The results were tested and confirmed in a further 50 subjects. The Markov chain Monte Carlo (MCMC) model fitting technique outperformed non-linear least squares. Using MCMC, the 2-fibre-orientation mono-exponential ball and stick model ( BSME2 ) provided artifact-free, stable results, in little processing time. The analogous ball and zeppelin model ( BZ2 ) also produced stable, low-noise parameter maps, though it required much greater computing resources (50 000 burn-in steps). In single-shell data, the gamma-distributed diffusivity ball and stick model ( BSGD2 ) underperformed relative to other models, despite being an often-used software default. It produced artifacts in the diffusivity maps even with extremely long processing times. Neither increased diffusion weighting nor a greater number of gradient orientations improved BSGD2 fits. In white matter (WM), the tensor produced the best fit as measured by Bayesian information criterion. This result contrasts with studies using multi-shell data. However, in crossing fibre regions the tensor confounded geometric effects with fractional anisotropy (FA): the planar/linear WM FA ratio was 49%, while BZ2 and BSME2 retained 76% and 83% of restricted fraction, respectively. As a result, the BZ2 and BSME2 models are strong candidates to optimize information extraction from single-shell dMRI studies.