TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
<|reference_start|>TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction: 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.<|reference_end|>
@article{guo2024tai-gan:,
    title={TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial
  Network for early-to-late frame conversion in dynamic cardiac PET inter-frame
  motion correction},
    author={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},
    journal={arXiv preprint arXiv:2402.09567},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09567},
    primaryClass={eess.IV cs.CV}
}