A model-based deconvolution approach to solve fiber crossing in diffusion-weighted MR imaging

Flavio Dell'Acqua, Giovanna Rizzo, Paola Scifo, Rafael Alonso Clarke, Giuseppe Scotti, Ferruccio Fazio

Research output: Contribution to journalArticlepeer-review


A deconvolution approach is presented to solve fiber crossing in diffusion magnetic resonance imaging. In order to provide a direct physical interpretation of the signal generation process, we started from the classical multicompartment model and rewrote this in terms of a convolution process, identifying a significant scalar parameter α to characterize the physical system response. Deconvolution is performed by a modified version of the Richardson-Lucy algorithm. Simulations show the ability of this method to correctly separate fiber crossing, even in the presence of noisy data, with lower signal-to-noise ratio, and imprecision in the impulse response function imposed during deconvolution. The in vivo data confirms the efficacy of this method to resolve fiber crossing in real complex brain structures. These results suggest the usefulness of our approach in fiber tracking or connectivity studies.

Original languageEnglish
Article number15
Pages (from-to)462-472
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number3
Publication statusPublished - Mar 2007


  • DTI
  • DW-MRI
  • Fiber crossing
  • Multicompartment model
  • Richardson-Lucy algorithm
  • Spherical deconvolution

ASJC Scopus subject areas

  • Biomedical Engineering


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