Rapid generation of biexponential and diffusional kurtosis maps using multi-layer perceptrons: A preliminary experience

Ludovico Minati

Research output: Contribution to journalArticlepeer-review

Abstract

Object: To investigate whether multi-layer perceptrons (MLPs) could be used to determine biexponential and diffusional kurtosis model parameters directly from diffusion-weighted images. Materials and 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. Conclusion: Multi-layer perceptrons are potentially useful as a curve fitting method for these models.

Original languageEnglish
Pages (from-to)299-305
Number of pages7
JournalMagnetic Resonance Materials in Physics, Biology, and Medicine
Volume21
Issue number4
DOIs
Publication statusPublished - Aug 2008

Keywords

  • Biexponential model
  • Diffusion-tensor imaging (DTI)
  • Diffusional kurtosis imaging (DKI)
  • Multi-layer perceptron (MLP)
  • Neural network (NN)

ASJC Scopus subject areas

  • Biophysics
  • Genetics

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