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

Abstract

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 smm-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.