Residual-based convolutional-neural-network (CNN) for low-dose CT denoising: impact of multi-slice input

Deep convolutional neural network (CNN) based methods have become popular choices for reducing image noise in CT. Some of these methods showed promising results, especially in terms of preserving natural CT noise texture. Early attempts of CNN denoising were based on 2D CNN models with either single-slice or 3-slice input. The 3-slice input was mainly to utilize the existing network architecture that were proposed for natural images with 3 input channels. Multi-slice input has the potential to incorporate spatial information from adjacent slices. However, it remains unknown if this strategy indeed improves the denoising performance compared to a 2D model with a single-slice input and what is the best network architecture to utilize the multi-slice input. Two categories of network architectures can be used for multi-slice input. First, multi-slice low-dose images can be stacked channelwise as multi-channel input to a 2D CNN model. Second, multi-slice images can be employed as the 3D volumetric input to a 3D CNN model, in which the 3D convolution layers are adopted. In this study, we compare the performance of multiple CNN models with 1, 3, and 7 input slices. For the 7-slice input, we also include a comparison between 2D and 3D CNN models. When the input channels of the 2D CNN model increases from 1 to 3 to 7, a trend of improved performance was observed. Comparing the two models with 7-slice input, the 3D model slightly outperforms the 2D model in terms of noise texture and homogeneity in liver parenchyma as well as better subjective visualization of vessels such as intrahepatic portal vein and jejunal artery.