We introduce a deep neural network scheme for predicting FDG-PET activity from a T1-weighted MR volume. This is useful for creating realistic anatomy-conforming synthetic PET data for prototyping of PET reconstruction algorithms, e.g. from abundant MR-only exam data. While deep networks can learn the average or nominal uptake patterns, in most cases, MR is ultimately incapable of fully predicting PET activity due to fundamental differences in the sensing modalities. We show, however, that these MR-derived “zero-dose” images can aid in differential contrast enhancement and visualization of PET by localizing and highlighting activity uniquely detected by the PET radiotracers.
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