Ferizi, U;
Schneider, T;
Panagiotaki, E;
Nedjati-Gilani, G;
Zhang, H;
Wheeler-Kingshott, CAM;
Alexander, DC;
(2013)
Ranking diffusion-MRI models with in-vivo human brain data.
In:
2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI).
(pp. 676 - 679).
IEEE
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Abstract
Diffusion MRI microstructure imaging provides a unique non-invasive probe into the microstructure of biological tissue. Its analysis relies on mathematical models relating microscopic tissue features to the MR signal. This work aims to determine which compartment models of diffusion MRI are best at describing the signal from in-vivo brain white matter. Recent work shows that three compartment models, including restricted intra-axonal, glial compartments and hindered extra-cellular diffusion, explain best multi b-value data sets from fixed rat brain tissue. Here, we perform a similar experiment using in-vivo human data. We compare one, two and three compartment models, ranking them with standard model selection criteria. Results show that, as with fixed tissue, three compartment models explain the data best, although simpler models emerge for the in-vivo data. We also find that splitting the scanning into shorter sessions has little effect on the models fitting and that the results are reproducible. The full ranking assists the choice of model and imaging protocol for future microstructure imaging applications in the brain.
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