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
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引用次数: 0
摘要
动态心脏正电子发射断层扫描(PET)中铷-82(82Rb)示踪剂的快速动力学和跨帧分布的高度变化给帧间运动校正带来了巨大挑战,特别是在早期帧中,传统的基于强度的图像配准技术并不适用。另外,一种很有前途的方法是利用生成方法来处理示踪剂分布的变化,以辅助现有的配准方法。为了改进帧配准和参数量化,我们提出了一种时间和解剖信息生成对抗网络(TAI-GAN),利用全对一映射将早期帧转换为晚期参考帧。具体来说,一个特征线性调制层对由时间示踪剂动力学信息生成的通道参数进行编码,而带有局部偏移的粗略心脏分割则作为解剖信息。我们在一个临床 82Rb PET 数据集上验证了我们提出的方法,发现我们的 TAI-GAN 可以生成图像质量很高的转换早期帧,可与真实参考帧相媲美。与使用原始帧相比,TAI-GAN 转换后的运动估计精度和临床心肌血流(MBF)定量都有所提高。我们的代码发布在 https://github.com/gxq1998/TAI-GAN。
TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction.
The rapid tracer kinetics of rubidium-82 (82Rb) 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 82Rb 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.