论文标题
基于深卷积神经网络的Spect Imaging重建方法
SPECT Imaging Reconstruction Method Based on Deep Convolutional Neural Network
论文作者
论文摘要
在本文中，我们探讨了一种新型的SPECT成像领域层合图像重建方法。深度学习方法和更具体的深卷卷卷神经网络（CNN）是在新的重建方法中采用的，该方法称为“ CNN重建-CNNR”。用于培训来自软件幻像的CNNR投影数据。为了评估CNNR方法的功效，使用了软件和硬件幻影。将所得的断层图像与通过过滤后的投影（FBP）[1]产生的图像，“最大似然期望最大化”（MLEM）[1]和有序的子集期望最大化（OSEM）[2]。
In this paper, we explore a novel method for tomographic image reconstruction in the field of SPECT imaging. Deep Learning methodologies and more specifically deep convolutional neural networks (CNN) are employed in the new reconstruction method, which is referred to as "CNN Reconstruction - CNNR". For training of the CNNR Projection data from software phantoms were used. For evaluation of the efficacy of the CNNR method, both software and hardware phantoms were used. The resulting tomographic images are compared to those produced by filtered back projection (FBP) [1], the "Maximum Likelihood Expectation Maximization" (MLEM) [1] and ordered subset expectation maximization (OSEM) [2].
