In this work, we demonstrate that Monte-Carlo simulations combined with fingerprint approaches can be used to develop decoding tools of the micro-structure using a dictionary learning approach. The validation has been done on a test object mimicking the mid-sagittal plane of a corpus callosum with axon diameters varying according to histological studies. The robustness of the decoding obviously depends on the richness of the dictionary, but, contrary to analytical approaches with highly non linear equations hard to fit practically, such MC approach do not have this kind of limitation, thus opening the way to decode more complex tissue cellular configurations.
This abstract and the presentation materials are available to members only; a login is required.