  Historically, patient datasets have been used to develop and validate various
reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm
development, without the need for acquiring hundreds of patient exams, in this
paper we demonstrate a deep learning technique to generate synthetic but
realistic whole-body PET sinograms from abundantly-available whole-body MRI.
Specifically, we use a dataset of 56 $^{18}$F-FDG-PET/MRI exams to train a 3D
residual UNet to predict physiologic PET uptake from whole-body T1-weighted
MRI. In training we implemented a balanced loss function to generate realistic
uptake across a large dynamic range and computed losses along tomographic lines
of response to mimic the PET acquisition. The predicted PET images are forward
projected to produce synthetic PET time-of-flight (ToF) sinograms that can be
used with vendor-provided PET reconstruction algorithms, including using
CT-based attenuation correction (CTAC) and MR-based attenuation correction
(MRAC). The resulting synthetic data recapitulates physiologic $^{18}$F-FDG
uptake, e.g. high uptake localized to the brain and bladder, as well as uptake
in liver, kidneys, heart and muscle. To simulate abnormalities with high
uptake, we also insert synthetic lesions. We demonstrate that this synthetic
PET data can be used interchangeably with real PET data for the PET
quantification task of comparing CT and MR-based attenuation correction
methods, achieving $\leq 7.6\%$ error in mean-SUV compared to using real data.
These results together show that the proposed synthetic PET data pipeline can
be reasonably used for development, evaluation, and validation of PET/MRI
reconstruction methods.
