基于形变估计与运动补偿的医学CT图像层间超分辨率算法

Medical CT images inter-slice super-resolution algorithm based ondeformation estimation and motion compensation

针对医学断层图像层间分辨率较低的问题,提出了基于形变估计与运动补偿的医学CT图像层间超分辨率算法用于生成切片间图像,从而提高层间分辨率。首先利用U-Net对相邻两幅图像作多尺度特征提取与融合;其次,为了处理层间图像的复杂形变,使用基于自适应协作流的变形扭曲模块来实现相邻切片间的双向形变估计,设计层级信息递进融合模块对金字塔特征层进行特征聚合,对生成图进行运动补偿;最后经过后处理网络以减少异常像素点。该算法在两种CT数据集上进行验证,平均PSNR值分别达到了35.59 dB和30.76 dB,输出图能较好地恢复图像细节。与现有的一些方法对比,相关实验证明了该算法的有效性。

Aiming at the problem of low interlayer resolution in medical tomographic images,this paper proposed an interlayer super-resolution algorithm based on deformation estimation and motion compensation.The method aimed to enhance interlayer resolution by generating interslice images.Firstly,the algorithm employed U-Net for multi-scale feature extraction and fusion of two adjacent images.To handle the complex deformation of interlayer images,it estimated slice bidirectional deformation by utilizing a warping module based on adaptive collaboration of flows.Multi-scale information fusion module performed feature aggregation on pyramid feature layers,to compensate for the motion of the generated map.Finally,it employed a post-processing network to reduce pixel outliers.The results on two CT datasets show that the PSNR of the proposed algorithm reaches 35.59 dB and 30.76 dB,the output effectively restores image details.Comparative experiments with existing methods demonstrate the effectiveness of the proposed algorithm.