基于HAMEN的稀疏投影能谱CT重建方法

Sparse-View Projection Spectral CT Reconstruction via HAMEN

能谱式计算机断层扫描(CT)可以提供不同能量下的衰减信息,有利于物质分解和组织分辨。稀疏投影可以有效降低辐射剂量,但会导致重建后的CT图像出现严重的伪影和噪声。基于卷积神经网络的深度学习重建方法虽在一定程度上改善了图像质量,却存在组织细节特征丢失严重等问题。提出一个基于混合注意力与多尺度特征融合相结合的边缘增强型网络(HAMEN)的能谱CT重建方法。首先利用边缘增强模块提取边缘特征并将其叠加到图像上,用于扩充输入图像信息;然后采用混合注意力模块分别生成通道注意力图和空间注意力图,以细化输入特征;并在网络的编码器处引入多尺度特征融合机制,增加跳跃连接以减少卷积层堆叠导致的特征丢失。实验结果表明,所提重建方法得到的CT图像的峰值信噪比可达37.64 dB,结构相似性指数达0.9935。此方法可在抑制稀疏投影导致的伪影和噪声的同时最大程度地保留组织细节信息,为后续的诊断等工作提供高质量图像。

Spectral computed tomography(CT)can provide attenuation information at different energy levels,which is essential for material decomposition and tissue discrimination.Sparse-view projection can effectively reduce radiation dose but can cause severe artifacts and noise in the reconstructed spectral CT images.Although deep learning reconstruction methods based on convolutional neural networks can improve the image quality,a loss in the tissue detail features is observed.Therefore,a spectral CT reconstruction method based on a hybrid attention module combined with a multiscale feature fusion edge enhancement network(HAMEN)is proposed.The network first extracts edge features of the input images through the edge enhancement module and concatenates them on the images,enriching the input image information.Next,a hybrid attention module is used to generate channel attention and spatial attention maps,which are used to refine the input features.The multiscale feature fusion mechanism is developed at the encoder,and some skip connections are added to minimize feature loss caused by the stacking of convolutional layers.The experimental results show that the peak signal-tonoise ratio of the CT images obtained using the proposed method is 37.64 dB,and the similarity structural index measure is 0.9935.This method can suppress artifacts and noise caused by sparse-view projection while preserving the tissue detail information.Furthermore,the CT image quality is improved for subsequent diagnosis and other works.