  Gating is commonly used in PET imaging to reduce respiratory motion blurring
and facilitate more sophisticated motion correction methods. In the
applications of low dose PET, however, reducing injection dose causes increased
noise and reduces signal-to-noise ratio (SNR), subsequently corrupting the
motion estimation/correction steps, causing inferior image quality. To tackle
these issues, we first propose a Siamese adversarial network (SAN) that can
efficiently recover high dose gated image volume from low dose gated image
volume. To ensure the appearance consistency between the recovered gated
volumes, we then utilize a pre-trained motion estimation network incorporated
into SAN that enables the constraint of gate-to-gate (G2G) consistency. With
high-quality recovered gated volumes, gate-to-gate motion vectors can be
simultaneously outputted from the motion estimation network. Comprehensive
evaluations on a low dose gated PET dataset of 29 subjects demonstrate that our
method can effectively recover the low dose gated PET volumes, with an average
PSNR of 37.16 and SSIM of 0.97, and simultaneously generate robust motion
estimation that could benefit subsequent motion corrections.
