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The rapid tracer kinetics of rubidium-82 ($^{82}$Rb) and high variation ofcross-frame distribution in dynamic cardiac positron emission tomography (PET)raise significant challenges for inter-frame motion correction, particularlyfor the early frames where conventional intensity-based image registrationtechniques are not applicable. Alternatively, a promising approach utilizesgenerative methods to handle the tracer distribution changes to assist existingregistration methods. To improve frame-wise registration and parametricquantification, we propose a Temporally and Anatomically Informed GenerativeAdversarial Network (TAI-GAN) to transform the early frames into the latereference frame using an all-to-one mapping. Specifically, a feature-wiselinear modulation layer encodes channel-wise parameters generated from temporaltracer kinetics information, and rough cardiac segmentations with local shiftsserve as the anatomical information. We validated our proposed method on aclinical $^{82}$Rb PET dataset and found that our TAI-GAN can produce convertedearly frames with high image quality, comparable to the real reference frames.After TAI-GAN conversion, motion estimation accuracy and clinical myocardialblood flow (MBF) quantification were improved compared to using the originalframes. Our code is published at https://github.com/gxq1998/TAI-GAN.

TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction