Experimentally and computationally fast method for estimation of a mean kurtosis

Abstract

The framework offers a robust and rapid method for estimating MK, with a protocol easily adapted on commercial scanners, as it requires only minimal modification of standard diffusion-weighting protocols. These properties make the method feasible in practically any clinical setting.

Mean Diffusional Kurtosis in Patients with Glioma: Initial Results with a Fast Imaging Method in a Clinical Setting

“…In the framework of the fast kurtosis sequence, 9 W , the mean of the kurtosis tensor W, is computed, and it was shown to be very similar numerically to the traditional MK. Here, we refer to it as MKЈ for convenience.…”

“…We recently proposed a kurtosis method 9,10 that is much faster than traditional kurtosis-acquisition schemes, 2,4 in terms of both acquisition and postprocessing times, and on the basis of this method we present here our first clinical results in 34 patients with glioma. We correlated MK values with histopathologic grades and compared them with those from the literature.…”

q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans

“…The network needs to be trained only once and can be applied to any number of datasets, taking 0.03 seconds per dataset, as opposed to several minutes per dataset required by most model fitting methods. Analytical solutions of scalar measure estimation [23] provide reduction of scan time and processing time comparable to q-DL, but are limited to specific scalar measures and acquisition schemes, as opposed to q-DL. A combination with multi-slice imaging is straightforward, yielding additional scan time reduction.…”

Efficient experimental designs for isotropic generalized diffusion tensor MRI (IGDTI)

“…The parameter W defined in Hansen et al (14) represents an approximation to the MK (15) and can be measured from two orientationally averaged DWIs without the need to explicitly compute the entire kurtosis tensor, W (15). As detailed in Appendix B, for fully symmetric diffusion, we can generalize the definition of W with respect to the HOTs D ðnÞ (10) and define the dimensionless rank-n tensor (Eq.…”

“…We evaluate the potential of IGDTI to measure, within clinically feasible scan durations, mADCs over a wide range of b-values and various rotation-invariant microstructural parameters, such as HOT-Traces (10,17), or the mean t-kurtosis (14). The proposed IGDTI framework may improve and accelerate experimental designs for clinical assessments with advanced diffusion MRI methods (13,23) and encourage the development of new, biologically specific, rotation-invariant parameters for quantifying tissue water mobilities in clinical applications.…”