Optimization of quasi-diffusion magnetic resonance imaging for quantitative accuracy and time-efficient acquisition

Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient, $$ in mm²  s^-1 and a fractional exponent, $$ , defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized.

Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between $$ and 5000 s mm^-2 . The effects of varying maximum b-value ( $$ ), number of b-value shells, and the effects of Rician noise were investigated.

QDTI measures showed $$ dependence, most significantly for $$ in white matter, which monotonically decreased with higher $$ leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of $$ and underestimation of $$ in white matter, and overestimation of $$ and $$ anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at $$  s mm^-2 , and 4 b-value shells at $$  s mm^-2 , providing minimal bias in $$ and $$ compared to the MbR.

A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.