A new tensor model for the measurement of diffusional anisotropy due to restricted diffusion

Abstract

Diffusion Tensor Imaging (DTI, Basser et al 1994) is the most widely used technique to measure the diffusion properties of the human brain tissues in-vivo. Although datasets comprising 30 samples, all but one of which acquired at a single b-value, are enough to estimate the diffusion tensor, in recent yearsnumerous acquisition schemes featuring more general diffusion encodings have been proposed. In order to vary the b-value, it is necessary to change either the gradient strength (G) or the duration or separation of the diffusion pulses. In the case of unrestricted diffusion (e.g. Gaussian), the b-value is theonly parameter that determines the diffusion-weighting. Thus, the same level of diffusion sensitization, hence the same signal attenuation, can be obtained by varying the gradient strength or the effective diffusion time (tau). In more realistic cases of diffusion, e.g., within restricted media (such as cells)changing G and tau have different effects on the signal. In this work, we compared the effect of changing the tau and the gradient strength G on the reconstruction error, using two tensor models: DTI, which is appropriate only for unrestricted diffusion, and the Diffusion Imaging with Confinement Tensor(DICT, Yolcu et al., Afzali et al 2015), which is applicable to both restricted and unrestricted diffusion scenarios. singthe either model, it is possible to estimate clinically important features, such as the Mean Diffusivity, and the Fractional Anisotropy (Afzali et al 2015, Pierpaoli and Basser 1996)