TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction.
TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction.
复制标题
TAI-GAN：用于动态心脏 PET 运动校正中早期到晚期帧转换的时间和解剖学信息 GAN。
DOI：
10.1007/978-3-031-44689-4_7
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Dvornek,NichaC
中科院分区：
文献类型：
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作者：
Guo,Xueqi;Shi,Luyao;Chen,Xiongchao;Zhou,Bo;Liu,Qiong;Xie,Huidong;Liu,Yi-Hwa;Palyo,Richard;Miller,EdwardJ;Sinusas,AlbertJ;Spottiswoode,Bruce;Liu,Chi;Dvornek,NichaC
The rapid tracer kinetics of rubidium-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 clinicalRb 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.
