The developments of PET/CT and PET/MR scanners provide opportunities for improving PET image quality by using anatomical information. In this paper, we propose a novel co-learning three-dimensional (3D) convolutional neural network (CNN) to extract modality-specific features from PET/CT image pairs and integrate complementary features into an iterative reconstruction framework to improve PET image reconstruction.

中文翻译：

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使用深度神经网络的解剖辅助 PET 图像重建

PET/CT 和 PET/MR 扫描仪的发展为通过使用解剖信息提高 PET 图像质量提供了机会。在本文中，我们提出了一种新的协同学习三维 (3D) 卷积神经网络 (CNN)，用于从 PET/CT 图像对中提取特定模态的特征，并将互补特征整合到迭代重建框架中以改进 PET 图像重建。