  Positron Emission Tomography (PET) is an important tool for studying
Alzheimer's disease (AD). PET scans can be used as diagnostics tools, and to
provide molecular characterization of patients with cognitive disorders.
However, multiple tracers are needed to measure glucose metabolism (18F-FDG),
synaptic vesicle protein (11C-UCB-J), and $\beta$-amyloid (11C-PiB).
Administering multiple tracers to patient will lead to high radiation dose and
cost. In addition, access to PET scans using new or less-available tracers with
sophisticated production methods and short half-life isotopes may be very
limited. Thus, it is desirable to develop an efficient multi-tracer PET
synthesis model that can generate multi-tracer PET from single-tracer PET.
Previous works on medical image synthesis focus on one-to-one fixed domain
translations, and cannot simultaneously learn the feature from multi-tracer
domains. Given 3 or more tracers, relying on previous methods will also create
a heavy burden on the number of models to be trained. To tackle these issues,
we propose a 3D unified anatomy-aware cyclic adversarial network (UCAN) for
translating multi-tracer PET volumes with one unified generative model, where
MR with anatomical information is incorporated. Evaluations on a multi-tracer
PET dataset demonstrate the feasibility that our UCAN can generate high-quality
multi-tracer PET volumes, with NMSE less than 15% for all PET tracers.
