Positron emission tomography (PET) imaging is a noninvasive technique that makes it possible to probe biological metabolic processes in vivo. However, PET image reconstruction is challenging due to the ill-posedness of the inverse problem. Many image reconstruction methods have been proposed over the past few years to improve diagnostic performance. However, most of these methods can compromise the reconstruction of important high-frequency structural details after aggressive denoising. To address this problem, in this work, we propose a novel deep learning method that reconstructs PET images using a cascading back-projection neural network (bpNet). This network consists of a domain translation operation, which acts as prior knowledge, cascaded with a modified encoder-decoder network. The image reconstruction pipeline ranges from the sinogram to the back-projection image and then to the PET image. Quantitative results from simulation data and Derenzo phantom experiments with the small animal PET prototype system developed in our laboratory clearly demonstrate that our proposed method provides favorable reconstructed image quality, especially for low-count PET image reconstruction.

中文翻译：

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使用级联反投影神经网络重建 PET 图像


正电子发射断层扫描（PET）成像是一种无创技术，可以探测体内的生物代谢过程。然而，由于逆问题的不适定性，PET 图像重建具有挑战性。在过去的几年里，人们提出了许多图像重建方法来提高诊断性能。然而，这些方法中的大多数都会在积极去噪后损害重要高频结构细节的重建。为了解决这个问题，在这项工作中，我们提出了一种新颖的深度学习方法，使用级联反投影神经网络（bpNet）重建 PET 图像。该网络由域转换操作组成，该操作充当先验知识，与修改后的编码器-解码器网络级联。图像重建流程的范围从正弦图到反投影图像，然后到 PET 图像。我们实验室开发的小动物 PET 原型系统的模拟数据和 Derenzo 模型实验的定量结果清楚地表明，我们提出的方法提供了良好的重建图像质量，特别是对于低计数 PET 图像重建。