A Model-Based Deconvolution Approach to Solve Fiber Crossing in Diffusion-Weighted MR Imaging

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, ident...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2007-03, Vol.54 (3), p.462-472
Main Authors: Dell'Acqua, Flavio, Rizzo, Giovanna, Scifo, Paola, Clarke, Rafael Alonso, Scotti, Giuseppe, Fazio, Ferruccio
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Brain - anatomy & histology
Brain modeling
Character generation
Cluster Analysis
Computer Simulation
Convolution
Deconvolution
Diffusion
Diffusion Magnetic Resonance Imaging - methods
DTI
DW-MRI
fiber crossing
Fibers
HARDI
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging
Imaging, Three-Dimensional - methods
Impulse response
In vivo
Magnetic resonance imaging
Mathematical models
Models, Anatomic
Models, Neurological
multicompartment model
Nerve Fibers - ultrastructure
Pattern Recognition, Automated - methods
Reproducibility of Results
Richardson-Lucy algorithm
Sensitivity and Specificity
Signal generation
Signal generators
Signal processing
Signal resolution
Signal to noise ratio
spherical deconvolution
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Summary: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 alpha 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
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2006.888830