The confinement tensor model improves characterization of diffusion-weighted magnetic resonance data with varied timing parameters
Diffusion imaging with confinement tensor (DICT) is a new model that employs a tensorial representation of the geometry confining the movements of water molecules. The model differs substantially from the commonly employed diffusion tensor imaging (DTI) technique even at small diffusion weightings when the dependence of the signal on the timing parameters of the pulse sequence is concerned. In this work, we assess the accuracy of the two models on a data set acquired from an excised monkey brain. The publicly available data set features differing values for diffusion pulse duration and separation. Our results indicate that the normalized mean squared error is reduced in an overwhelming portion of the voxels when the DICT model is employed, suggesting the superiority of DICT in characterizing the temporal dependence of the diffusion process in nervous tissue.