Keywords: Analysis/Processing, Microstructure, Histology, Diffusion Imaging, g-ratio, axon diameter
Motivation: Machine learning approaches are an alternative to conventional biophysical model fitting used to generate MRI microstructural maps, but the lack of paired MRI-histology data complicates end-to-end training of these models.
Goal(s): Develop a nonparametric deep learning based prediction of joint distributions of g-ratios and axon diameters from multimodal MRI data.
Approach: Histology-based synthetic MRI data was used to pretrain a conditioned normalizing flow model. Transfer learning was then performed on limited paired MRI-histology data.
Results: The joint distribution shows good visual agreement with actual samples and the distances between the marginal probabilities and their respective samples exhibit a Jensen-Shannon distance smaller than 0.22.
Impact: We present an optimized model to obtain non-parametric joint distributions of g-ratios and axon diameters from multimodal MRI from limited experimental data. The approach can easily be adapted to other microstructural modeling tasks.
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