TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
| Autor: | Guo, Xueqi, Shi, Luyao, Chen, Xiongchao, Liu, Qiong, Zhou, Bo, Xie, Huidong, Liu, Yi-Hwa, Palyo, Richard, Miller, Edward J., Sinusas, Albert J., Staib, Lawrence H., Spottiswoode, Bruce, Liu, Chi, Dvornek, Nicha C. |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Under revision at Medical Image Analysis |
| Databáze: | arXiv |
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