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DEEP LEARNING-BASED PET SCATTER ESTIMATION SYSTEMS AND METHODS USING AN INCEPTION NEURAL NETWORK MODEL

机译:基于深度神经网络的基于深度学习的PET散射估计系统及方法

摘要

Systems, apparatus, methods, and computer-readable storage media to estimate scatter in an image are disclosed. An example apparatus includes a network generator to generate and train an inception neural network using first and second input to deploy an inception neural network model to process image data when first and second outputs of the inception neural network converge, the first input based on a raw sinogram of a first image and the second input based on an attenuation-corrected sinogram of the first image, the inception neural network including a first filter of a first size and a second filter of a second size in a layer to process the first input and/or the second input to generate an estimate of scatter in the first image. The example apparatus also includes an image processor to apply the estimate of scatter to a second image to generate a processed image.
机译:公开了用于估计图像中的散射的系统,装置,方法和计算机可读存储介质。示例设备包括网络生成器,该网络生成器使用第一和第二输入来生成和训练初始神经网络,以在初始神经网络的第一和第二输出收敛时部署初始神经网络模型以处理图像数据,第一输入基于原始基于第一图像的经衰减校正的正弦图的第一图像和第二输入的正弦图,初始神经网络在层中包括第一大小的第一滤波器和第二大小的第二滤波器,以处理第一输入和/或第二输入以生成第一图像中的散射估计。该示例装置还包括图像处理器,该图像处理器将散射的估计应用于第二图像以生成处理后的图像。

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