Dual-task learning for low-dose CT simulation and denoising

Deep learning (DL) are being extensively investigated for low-dose computed tomography (CT). The success of DL lies in the availability of big data, learning the non-linear mapping of low-dose CT to target images based on convolutional neural networks. However, due to the commercial confidentiality of CT vendors, there are very few publicly raw projection data available to simulate paired training data, which greatly reduces the generalization and performance of the network. In the paper, we propose a dual-task learning network (DTNet) for low-dose CT simulation and denoising at arbitrary dose levels simultaneously. The DTNet can integrate low-lose CT simulation and denoising into a unified optimization framework by learning the joint distribution of low-dose CT and normal-dose CT data. Specifically, in the simulation task, we propose to train the simulation network by learning a mapping from normal-dose to low-dose at different levels, where the dose level can be continuously controlled by a noise factor. In the denoising task, we propose a multi-level low-dose CT learning strategy to train the denoising network, learning many-to-one mapping. The experimental results demonstrate the effectiveness of our proposed method in low-dose CT simulation and denoising at arbitrary dose levels.