Diffusion-tensor-based method for robust and practical estimation of axial and radial diffusional kurtosis.

OBJECTIVES

A new method that can estimate diffusional kurtosis image (DKI), estimated DKI (eDKI), parallel and perpendicular to neuronal fibres from greatly limited image data was designed to enable quick and practical assessment of DKI in clinics. The purpose of this study was to discuss the potential of this method for clinical use.

METHODS

Fourteen healthy volunteers were examined with a 3-Tesla MRI. The diffusion-weighting parameters included five different b-values (0, 500, 1,500, 2,000 and 2,500 s/mm(2)) with 64 different encoding directions for each of the b-values. K values were calculated by both conventional DKI (convDKI) and eDKI from these complete data, and also from the data that the encoding directions were abstracted to 32, 21, 15, 12 and 6. Error-pixel ratio and the root mean square error (RMSE) compared with the standard were compared between the methods (Wilcoxon signed-rank test: P < 0.05 was considered significant).

RESULTS

Error-pixel ratio was smaller in eDKI than in convDKI and the difference was significant. In addition, RMSE was significantly smaller in eDKI than in convDKI, or otherwise the differences were not significant when they were obtained from the same data set.

CONCLUSION

eDKI might be useful for assessing DKI in clinical settings.

KEY POINTS

• A method to practically estimate axial/radial DKI from limited data was developed. • The high robustness of the proposed method can greatly improve map images. • The accuracy of the proposed method was high. • Axial/radial K maps can be calculated from limited diffusion-encoding directions. • The proposed method might be useful for assessing DKI in clinical settings.