TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late
  frame conversion in dynamic cardiac PET motion correction
Xueqi Guo Luyao Shi Xiongchao Chen Bo Zhou Qiong Liu Huidong Xie Yi-Hwa Liu Richard Palyo Edward J. Miller Albert J. Sinusas Bruce Spottiswoode Chi Liu Nicha C. Dvornek
The rapid tracer kinetics of rubidium-82 ($^{82}$Rb) and high variation of
cross-frame distribution in dynamic cardiac positron emission tomography (PET)
raise significant challenges for inter-frame motion correction, particularly
for the early frames where conventional intensity-based image registration
techniques are not applicable. Alternatively, a promising approach utilizes
generative methods to handle the tracer distribution changes to assist existing
registration methods. To improve frame-wise registration and parametric
quantification, we propose a Temporally and Anatomically Informed Generative
Adversarial Network (TAI-GAN) to transform the early frames into the late
reference frame using an all-to-one mapping. Specifically, a feature-wise
linear modulation layer encodes channel-wise parameters generated from temporal
tracer kinetics information, and rough cardiac segmentations with local shifts
serve as the anatomical information. We validated our proposed method on a
clinical $^{82}$Rb PET dataset and found that our TAI-GAN can produce converted
early frames with high image quality, comparable to the real reference frames.
After TAI-GAN conversion, motion estimation accuracy and clinical myocardial
blood flow (MBF) quantification were improved compared to using the original
frames. Our code is published at https://github.com/gxq1998/TAI-GAN.