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.
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