Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data, whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases. Especially, our method has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection.

The wide applications of X-ray computed tomography (CT) bring low dose CT (LDCT) into a clinical prerequisite. But reducing radiation exposure in CT often leads to significantly increased noise and artifacts, which might lower the judgement accuracy of radiologists. In this paper, we put forward a domain progressive 3D residual convolution network (DP-ResNet) for LDCT imaging procedure which contains sinogram domain network (SD-net), filtered back projection (FBP) and image domain network (ID-net) three stages. Though both based on the residual network structure, the SD-net and ID-net provide complementary effect on improving the final LDCT quality. Experimental results with both simulated and real projection data show that this domain progressive deep learning network achieves significantly improved performance by combing the network processing in the two domains.

The x-ray exposure to patients has become a major concern in computed tomography (CT) and minimizing the radiation exposure has been one of the major efforts in the CT field. Due to plenty high-attenuation tissues in the human chest, under low-dose scan protocols, thoracic low-dose CT (LDCT) images tend to be severely degraded by excessive mottled noise and non-stationary streak artifacts. Their removal is rather a challenging task because the streak artifacts with directional prominence are often hard to discriminate from the attenuation information of normal tissues. This paper describes a two-step processing scheme called 'artifact suppressed large-scale nonlocal means' for suppressing both noise and artifacts in thoracic LDCT images. Specific scale and direction properties were exploited to discriminate the noise and artifacts from image structures. Parallel implementation has been introduced to speed up the whole processing by more than 100 times. Phantom and patient CT images were both acquired for evaluation purpose. Comparative qualitative and quantitative analyses were both performed that allows conclusion on the efficacy of our method in improving thoracic LDCT data.

X-ray Computed Tomography (CT) is one of the most widely used tools in medical imaging, industrial non-destructive testing, lesion detection and other applications. However, decreasing the projection number to lower the X-ray radiation dose usually leads to severe streak artifacts. To improve the quality of the images reconstructed from sparse-view projection data, we developed a hybrid domain neural network (HDNet) processing for sparse-view CT reconstruction in this study. The HDNet decomposes the sparse-view CT reconstruction problem into two stages and each stage focuses on one mission, which reduces the learning difficulty of the entire network. Experiments based on the simulated and clinical datasets are performed to demonstrate the performance of the proposed method. Compared with other competitive algorithms, quantitative and qualitative results show that the proposed method makes great improvement on artifact suppression, tiny structure restoration and contrast retention.