For incomplete scanning data, the traditional algorithm cannot guarantee that the medical computed tomography (CT) reconstruction image meets diagnostic requirements. According to the compressed sensing theory, the medical CT image with sparse representation can be reconstructed from the incomplete scanning data, providing reliable information for diagnosis. From the perspective of reconstruction, this paper proposes an image reconstruction algorithm based on total variation and convolutional sparse coding in gradient domain. Gradient domain convolutional sparse coding is to apply gradient constraint to feature images, and gradient regularization constraint is used to suppress outliers, which solves the problems of structure loss and new artifacts caused by the inaccurate filter. The proposed algorithm directly processes the whole image to obtain the correlation of local neighborhoods, and uses the global correlation of gradient images to generate better edge and clear gradient image features, which can effectively capture the local features of the image. In addition, by introducing total variation as the regularization term, the micro structures and details of the image can be further restored and the noise can be effectively suppressed. Qualitative and quantitative experimental results show that, compared with other algorithms, the proposed algorithm retains more details and has higher reconstruction quality, which verifies the effectiveness of the method.