The confinement tensor model improves characterization of diffusion-weighted magnetic resonance data with varying time parameters

Diffusion imaging with confinement tensor (DICT) is a newmodel that employs a tensorial representation of the geometryconfining the movements of water molecules. The model differssubstantially from the commonly employed diffusion tensorimaging (DTI) technique even at small diffusion weightingswhen the dependence of the signal on the timing parametersof the pulse sequence is concerned. In this work,we assess the accuracy of the two models on a data set acquiredfrom an excised monkey brain. The publicly availabledata set features differing values for diffusion pulse durationand separation. Our results indicate that the normalized meansquared error is reduced in an overwhelming portion of thevoxels when the DICT model is employed, suggesting the superiorityof DICT in characterizing the temporal dependenceof the diffusion process in nervous tissue.