TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial
  Network for early-to-late frame conversion in dynamic cardiac PET inter-frame
  motion correction
Xueqi Guo Luyao Shi Xiongchao Chen Qiong Liu Bo Zhou Huidong Xie Yi-Hwa Liu Richard Palyo Edward J. Miller Albert J. Sinusas Lawrence H. Staib Bruce Spottiswoode Chi Liu Nicha C. Dvornek
Inter-frame motion in dynamic cardiac positron emission tomography (PET)
using rubidium-82 (82-Rb) myocardial perfusion imaging impacts myocardial blood
flow (MBF) quantification and the diagnosis accuracy of coronary artery
diseases. However, the high cross-frame distribution variation due to rapid
tracer kinetics poses a considerable challenge for inter-frame motion
correction, especially for early frames where intensity-based image
registration techniques often fail. To address this issue, we propose a novel
method called Temporally and Anatomically Informed Generative Adversarial
Network (TAI-GAN) that utilizes an all-to-one mapping to convert early frames
into those with tracer distribution similar to the last reference frame. The
TAI-GAN consists of a feature-wise linear modulation layer that encodes
channel-wise parameters generated from temporal information and rough cardiac
segmentation masks with local shifts that serve as anatomical information. Our
proposed method was evaluated on a clinical 82-Rb PET dataset, and the results
show that our TAI-GAN can produce converted early frames with high image
quality, comparable to the real reference frames. After TAI-GAN conversion, the
motion estimation accuracy and subsequent myocardial blood flow (MBF)
quantification with both conventional and deep learning-based motion correction
methods were improved compared to using the original frames.