Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration methods are generally computationally intense and time-consuming. Deep learning approaches are promising in achieving high accuracy with fast speed, but have yet been investigated with consideration for tracer distribution changes or in the whole-body scope. In this work, we developed an unsupervised automatic deep learning-based framework to correct inter-frame body motion. The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer, fully utilizing dynamic temporal features and spatial information. Our dataset contains 27 subjects each under a 90-min FDG whole-body dynamic PET scan. Evaluating performance in motion simulation studies and a 9-fold cross-validation on the human subject dataset, compared with both traditional and deep learning baselines, we demonstrated that the proposed network achieved the lowest motion prediction error, obtained superior performance in enhanced qualitative and quantitative spatial alignment between parametric $K_i$ and $V_b$ images, and significantly reduced parametric fitting error. We also showed the potential of the proposed motion correction method for impacting downstream analysis of the estimated parametric images, improving the ability to distinguish malignant from benign hypermetabolic regions of interest. Once trained, the motion estimation inference time of our proposed network was around 460 times faster than the conventional registration baseline, showing its potential to be easily applied in clinical settings.

全身动态 PET 中的主体运动会引入帧间失配并严重影响参数成像。传统的非刚性配准方法通常计算量大且耗时。深度学习方法有望快速实现高精度，但尚未在考虑示踪剂分布变化或全身范围的情况下进行研究。在这项工作中，我们开发了一种基于无监督自动深度学习的框架来纠正帧间身体运动。运动估计网络是具有组合卷积长短期记忆层的卷积神经网络，充分利用动态时间特征和空间信息。我们的数据集包含 27 名受试者，每名受试者都接受了 90 分钟的 FDG 全身动态 PET 扫描。通过评估运动模拟研究的性能以及对人类受试者数据集的 9 倍交叉验证，与传统和深度学习基线相比，我们证明所提出的网络实现了最低的运动预测误差，在增强的定性和定量方面获得了优异的性能参数之间的空间对齐${\mathrm{<span\; data-immersive-translate-walked="20eb31f9-4e70-4f4e-91ac-df375e3b084b">K</span>}}_{\mathrm{<span\; data-immersive-translate-walked="20eb31f9-4e70-4f4e-91ac-df375e3b084b">我</span>}}$和${\mathrm{<span\; data-immersive-translate-walked="20eb31f9-4e70-4f4e-91ac-df375e3b084b">V</span>}}_{\mathrm{<span\; data-immersive-translate-walked="20eb31f9-4e70-4f4e-91ac-df375e3b084b">乙</span>}}$图像，并显着减少参数拟合误差。我们还展示了所提出的运动校正方法对影响估计参数图像的下游分析的潜力，提高了区分恶性和良性代谢亢进区域的能力。 经过训练后，我们提出的网络的运动估计推理时间比传统配准基线快约 460 倍，显示出其在临床环境中轻松应用的潜力。