Reconstruction of few-view x-ray Computed Tomography (CT) data is a highly ill-posed problem. It is often used in applications that require low radiation dose in clinical CT, rapid industrial scanning, or fixed-gantry CT. Existing analytic or iterative algorithms generally produce poorly reconstructed images, severely deteriorated by artifacts and noise, especially when the number of x-ray projections is considerably low. This paper presents a deep network-driven approach to address extreme few-view CT by incorporating convolutional neural network-based inference into state-of-the-art iterative reconstruction. The proposed method interprets few-view sinogram data using attention-based deep networks to infer the reconstructed image. The predicted image is then used as prior knowledge in the iterative algorithm for final reconstruction. We demonstrate effectiveness of the proposed approach by performing reconstruction experiments on a chest CT dataset. Computed Tomography (CT) reconstruction is an inverse problem where images are reconstructed from a collection of multiple x-ray projections known as sinogram. Conventional CT imaging systems use densely sampled x-ray projections (roughly equal to one projection per detector column) with a full angular range (180-360 degrees). Unlike the conventional CT setup, on the other hand, some CT systems use different imaging configurations that require rapid scanning or reduced radiation dose. In those cases, the CT imaging uses a small number of x-ray projections, referred to as few-view CT. Reconstructing images from a few x-ray projections becomes an extremely under-determined inverse problem, which results in significant image degradation. The reconstructed images from extremely few-view sinogram measurement (10 views or less) are often characterized by severe artifacts and noise, even with state-of-the-art regularized iterative algorithms [1, 2, 3, 4, 5] as well as with the widely used Filtered Backprojection (FBP) [6] . In recent years, deep learning-based approaches have been successfully applied to a number of image restoration, denoising, inpainting and other image processing applications. Methods in this category use perceptual information as well as contextual features to improve the image quality. In CT imaging applications, several deep convolutional neural network (CNN) approaches have been proposed to address different ill-conditioned CT reconstruction applications. Methods in [7, 8, 9] proposed CNN-based approaches to improve the image quality for low-dose (sparse-view) imaging. These approaches aim to infer the noise distribution to generate a cleaner image from the noisy image. However, these methods do not employ the sinogram to ensure that the reconstructed image is consistent with the measurement. Gupta et al. [10] proposed a method using a CNN-based projector for moderate sparse-view reconstruction (45 and 144 views). Anirudh et al. [11] proposed a CNN-based sinogram completion approach to address limited-angle CT reconstruction. In this paper, we present a CNN inference-based reconstruction algorithm to address extremely few-view CT imaging scenarios. For the initial reconstruction, we employ a CNN-based inference model, based on CT-Net [11] , that directly uses the input measurement (few-view sinogram data) to predict the reconstructed image. In the cases where the sinogram measurements are extremely undersampled, the images reconstructed by existing analytic and iterative methods may suffer from too much noise with little high frequency information, and the methods in [7, 8, 9 ] may repair the missing or noisy part with perceptually created, but incorrect content. Thus, we pursue a method that directly uses the sinogram so that the reconstructed content is consistent with the input measurement, as an inverse problem. Furthermore, instead of performing the sinogram completion in [11] optimized for limited-angle reconstruction, we propose to use the predicted image from the CNN inference model as an image prior in state-of-the-art iterative algorithms in order to improve the final reconstruction. Our experiments on a chest CT dataset show that the proposed model outperforms existing analytical and state-of-the-art iterative algorithms as well as the sinogram completion.
We present a CNN inference-based reconstruction algorithm to address extremely few-view CT. 
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