Alzheimer’s disease (AD) is a neurodegenerative disorder where functional deficits precede structural deformations. Various studies have demonstrated the efficacy of deep learning in diagnosing AD using imaging data, and that functional modalities are more helpful than structural counterparts over comparable sample size. To deal with the lack of large-scale functional data in the real world, we used a structure-to-function translation network to artificially generate a previously non-existent spatially-matched functional neuroimaging dataset from existing large-scale structural data. The artificial functional data, generated with little cost, complemented the authentic structural data to further improve the performance of AD classification.
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