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Recent development of compressed sensing (CS) and deep learning (DL) brought a significant progress in image reconstruction for sparse-view CT and low-dose CT. However, there still exist a strong demand in further improving image quality. We propose a new framework for image reconstruction in sparse-view CT and low-dose CT, which significantly outperforms CS and DL in terms of image quality. This advantage originates from combining CS and DL in a successful way as described below, thereby leading to compensating for each other’s weakness. The proposed framework is based on the following principle. First, CS image reconstruction using TV (or Nonlocal TV) regularization is performed with prespecified M different values of regularization parameters (β1, β2, ---,βM), which generates M reconstructed images (z1,z2, - --,zM) with varying degree of TV smoothing. Next, the TV images (z1,z2, ---,zM) together with a FBP reconstruction (no smoothing) y are inputted into CNN having M+1 input channels and single output channel. The final reconstructed image is obtained as the output of CNN. With respect to the learning of network, CNN parameters (weights and biases) are estimated by minimizing an MSE loss function using learning data, i.e. a set of M+1 input images and corresponding answer image. In our previous work [11], we have already proposed a similar framework for the case where the number of input TV image is one. However, we expect that increasing the number of input images as mentioned above will further improve image quality. In this work, we have investigated such a new extension. Intuitively, the proposed method is based on combining good parts in M+1 input images to synthesizing a higher quality image, and this synthesis is performed by using DL. We have performed a simulation study using a dataset of clinical abdominal CT images for 2-D low-dose CT and 2-D sparse-view CT. The result demonstrates that the proposed combined approach is able to significantly improve image quality compared to the case where CS or DL was used alone, both in terms of numerical evaluation (RMSE and SSIM) and visual evaluation.
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