X射线CT (computed tomography)成像技术中常遇到尺寸、形状或是材料特殊的检测对象, 此时只能在有限的角度内进行数据采集. 在不满足CT 精确重建条件时, 常规的重建算法表现出严重的滑坡伪影, 难以为实际应用提供有价值的信息参考. 为了更好地消减图像中的伪影, 文章提出了一种基于ResNet网络模型的有限角CT图像 重建方法, 其采用残差学习方法, 可用来对输入的图像进行特征提取, 获取足够的细 节信息, 并使用反卷积算法对学习的特征进行恢复. 有限角医学CT图像数据证明了所提出的重建方法有效性, 明显减少了滑坡伪影且有效地保留了原始图像的结构特征.

In $x$-ray CT (computed tomography) imaging technology, there often exist the detection objects with the special sizes, shapes or materials, where the projection data can are only collected in some limited projection angles. In this case, the obtained data does not meet CT accurate reconstruction conditions. The reconstructed CT images using the conventional algorithm show some serious landslide artifacts, which are difficult to provide valuable information for many practical applications. In order to better suppress the landslide artifacts, this paper proposes the limited-angle CT image reconstruction method based on the ResNet model, in which the used residual learning method can extract the features of the input images and capture enough detail information, and then the deconvolution algorithm is used to restore the learned features. The numerical experiments prove the effectiveness of the proposed reconstruction method, which can significantly reduce the landslide artifacts and effectively retain the structural features of CT images. The reconstruction results can provide high quality CT images for clinical diagnosis.