The distribution of fiber population in the whole brain can be inferred from the samples generated by tractography on diffusion MRI. In this paper, we modeled the distribution of fiber population globally based on representation learning method. Using deep neural networks, we performed dimension reduction on the fiber tracts, and modeled the fiber population by a probability distribution over a latent space in lower dimension. This method enabled us to identify tracts distributed with different densities when compared with another tractogram, and can thus be used to identify structural difference or to detect spurious tracts caused by probabilistic tractography.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords