论文标题
低剂量CT图像重建的动量网络
Momentum-Net for Low-Dose CT Image Reconstruction
论文作者
论文摘要
本文采用了最新的快速迭代神经网络框架动量 - 网络，使用适当的模型来低剂量X射线计算机断层扫描（LDCT）图像重建。在提议的动量网络的每一层，基于模型的图像重建模块求解了主要惩罚的加权最小二乘问题，并且图像提炼模块使用四层卷积神经网络（CNN）。 NIH AAPM-MAYO诊所低剂量CT大挑战数据集的实验结果表明，与最先进的非涉及非涉及图像Denoing Denoing Denoing Deep Naural Network（NN），Vavresnet（LDCT）相比，提出的动量网络结构显着提高了图像重建精度。我们还研究了适用于图像精炼NN学习以满足非专业nn特性的光谱归一化技术。但是，实验结果表明，这并不能改善动量网络的图像重建性能。
This paper applies the recent fast iterative neural network framework, Momentum-Net, using appropriate models to low-dose X-ray computed tomography (LDCT) image reconstruction. At each layer of the proposed Momentum-Net, the model-based image reconstruction module solves the majorized penalized weighted least-square problem, and the image refining module uses a four-layer convolutional neural network (CNN). Experimental results with the NIH AAPM-Mayo Clinic Low Dose CT Grand Challenge dataset show that the proposed Momentum-Net architecture significantly improves image reconstruction accuracy, compared to a state-of-the-art noniterative image denoising deep neural network (NN), WavResNet (in LDCT). We also investigated the spectral normalization technique that applies to image refining NN learning to satisfy the nonexpansive NN property; however, experimental results show that this does not improve the image reconstruction performance of Momentum-Net.
