Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the list-mode PET data into multiple bins over a respiratory cycle. Non-rigid registration of respiratory-gated PET images can reduce motion artifacts and preserve count statistics, but it is time consuming. In this work, we propose an unsupervised non-rigid image registration framework using deep learning for motion correction. Our network uses a differentiable spatial transformer layer to warp the moving image to the fixed image and uses a stacked structure for deformation field refinement. Estimated deformation fields were incorporated into an iterative image reconstruction algorithm to perform motion compensated PET image reconstruction. We validated the proposed method using simulation and clinical data and implemented an iterative image registration approach for comparison. Motion compensated reconstructions were compared with ung...

中文翻译：

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使用基于深度学习的图像配准框架对呼吸门控 PET 图像进行运动校正。


PET 数据采集过程中患者呼吸和运动引起的伪影会影响图像质量。呼吸门控通常用于在一个呼吸周期内将列表模式 PET 数据门控到多个容器中。呼吸门控 PET 图像的非刚性配准可以减少运动伪影并保留计数统计数据，但非常耗时。在这项工作中，我们提出了一种使用深度学习进行运动校正的无监督非刚性图像配准框架。我们的网络使用可微分空间变换层将运动图像扭曲为固定图像，并使用堆叠结构进行变形场细化。将估计的变形场纳入迭代图像重建算法中，以执行运动补偿 PET 图像重建。我们使用模拟和临床数据验证了所提出的方法，并实施了迭代图像配准方法进行比较。运动补偿重建与非...