In vivo generalized diffusion tensor imaging (GDTI) using higher-order tensors (HOT).
Generalized diffusion tensor imaging (GDTI) using higher-order tensor (HOT) statistics generalizes the technique of diffusion tensor imaging by including the effect of nongaussian diffusion on the signal of MRI. In GDTI-HOT, the effect of nongaussian diffusion is characterized by higher-order tensor statistics (i.e., the cumulant tensors or the moment tensors), such as the covariance matrix (the second-order cumulant tensor), the skewness tensor (the third-order cumulant tensor), and the kurtosis tensor (the fourth-order cumulant tensor). Previously, Monte Carlo simulations have been applied to verify the validity of this technique in reconstructing complicated fiber structures. However, no in vivo implementation of GDTI-HOT has been reported. The primary goal of this study is to establish GDTI-HOT as a feasible in vivo technique for imaging nongaussian diffusion. We show that probability distribution function of the molecular diffusion process can be measured in vivo with GDTI-HOT and be visualized with three-dimensional glyphs. By comparing GDTI-HOT to fiber structures that are revealed by the highest resolution diffusion-weighted imaging (DWI) possible in vivo, we show that the GDTI-HOT can accurately predict multiple fiber orientations within one white matter voxel. Furthermore, through bootstrap analysis we demonstrate that in vivo measurement of HOT elements is reproducible, with a small statistical variation that is similar to that of diffusion tensor imaging.
Liu
Chunlei
C
Brain Imaging and Analysis Center, Duke University, Durham, North Carolina 27705, USA. chunlei.liu@duke.edu
Mang
Sarah C
SC
Moseley
Michael E
ME
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity