In this work, we develop a neural network-based method to convert a noisy
motion signal generated from segmenting rebinned list-mode cardiac SPECT
images, to that of a high-quality surrogate signal, such as those seen from
external motion tracking systems (EMTs). This synthetic surrogate will be used
as input to our pre-existing motion correction technique developed for EMT
surrogate signals. In our method, we test two families of neural networks to
translate noisy internal motion to external surrogate: 1) fully connected
networks and 2) convolutional neural networks. Our dataset consists of cardiac
perfusion SPECT acquisitions for which cardiac motion was estimated (input:
center-of-count-mass - COM signals) in conjunction with a respiratory surrogate
motion signal acquired using a commercial Vicon Motion Tracking System (GT: EMT
signals). We obtained an average R-score of 0.76 between the predicted
surrogate and the EMT signal. Our goal is to lay a foundation to guide the
optimization of neural networks for respiratory motion correction from SPECT
without the need for an EMT.

以神经网络为基础，将从重排列列表模式心脏 SPECT 图像分割生成的嘈杂运动信号转换为高质量的替代信号，以用于对外部运动跟踪系统（EMTs）测量的运动矫正技术，并测试两种神经网络用于翻译无噪声内部运动到外部替代，是对于呼吸道运动校正从 SPECT 的优化提供基础。