Rapid generation of biexponential and diffusional kurtosis maps using multi-layer perceptrons: a preliminary experience.
OBJECTIVE
To investigate whether multi-layer perceptrons (MLPs) could be used to determine biexponential and diffusional kurtosis model parameters directly from diffusion-weighted images.


METHODS
Model parameters were determined with least-squares fitting and with MLPs. The corresponding estimates were compared with linear regressions, t tests and Levene's tests. Residuals were also compared.


RESULTS
Strong linear correlation was found for all parameters. MLP estimates were unbiased for the biexponential but not for the kurtosis model, and generally had smaller variance. Residuals were smaller for MLP estimates. The maps generated by the two methods were visually very similar.


CONCLUSIONS
Multi-layer perceptrons are potentially useful as a curve fitting method for these models.
Minati
Ludovico
L
Fondazione Istituto Nazionale Neurologico Carlo Besta, via Celoria 11, Milano, Italy. lminati@istituto-besta.it
D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D016014:Linear Models; D008962:Models, Theoretical