Keywords: Susceptibility, Quantitative Susceptibility mapping
Guiding the network architecture to learn to apply the kernel inversely in Fourier space allows training to be less prone to overfitting. Using simulated images from a single brain image, it is possible to satisfactorily reconstruct a susceptibility map of the abdomen. By having as input the Fourier space of the local field and the kernel of the dipole, the network learned to reduce the noise, to divide the data of the local field by the kernel where possible and to recover the data in the magic cone.
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