Low-dose dental CT image enhancement using a multiscale feature sensing network

Purpose: The damage to humans caused by CT radiation dose has always been a health concern. Consequently, people prefer to use low-dose CT for examinations. A common method of dose reduction in CT is to reduce the number of samples of the projection data. However, when the dose is reduced by downsampling, artifacts and noise appear in the reconstructed CT images, degrading their quality significantly and resulting in information loss. In this work, we propose a deep learning-based method to improve the quality of low-dose dental CT images. Method: The proposed method involves a new deep neural network whose backbone is composed of two blocks; each basic block in network is designed to have three paths that sense and capture image features at different scales. The large-scale feature information is used to correct the extraction and learning of the smaller-scale information. Moreover, a shuffling operation is introduced to better preserve the feature information when expanding the size of the feature map. Results: We compare the proposed network with the DnCNN and REDCNN networks on a clinical dental CT dataset. The comparison and analysis of various quantitative results and image details in the experiment show that the proposed network corrects artifacts, suppresses noise and preserves image detail information more accurately than do the compared networks. In addition, the proposed network alleviates the over-smoothing phenomenon in the restored images to a certain extent. Conclusions: We conducted research on incompletely sampled dental CT data and proposed a multiscale feature-sensing deep neural network to improve the quality of low-dose dental CT images. The experimental results demonstrate that the proposed network effectively enhances the quality of low-dose dental CT images, making it helpful for promoting wider applications of low-dose technology in cone-beam dental CT.