Streak artifacts reduction algorithm using an implicit neural representation in sparse-view CT

Abstract

Sparse-view computed tomography (CT) has been attracting attention for its reduced radiation dose and scanning time. However, analytical image reconstruction methods such as filtered back-projection (FBP) suffer from streak artifacts due to sparse-view sampling. Because the streak artifacts are deterministic errors, we argue that the same artifacts can be reasonably estimated using a prior image (i.e., smooth image of the same patient) with known imaging system parameters. Based on this idea, we reconstruct an FBP image with sparse-view projection data, regenerate the streak artifacts by forward and back-projection of a prior image with sparse views, and then subtract them from the original FBP image. For the success of this approach, the prior image needs to be patient specific and easily obtained from given sparse-view projection data. Therefore, we introduce a new concept of implicit neural representations for modeling attenuation coefficients. In the implicit neural representations, neural networks output a patient-specific attenuation coefficient value for an input pixel coordinate. In this way, network’s parameters serve as an implicit representation of a CT image. Unlike conventional deep learning approaches that utilize a large, labeled dataset, an implicit neural representation is optimized using only sparse-view projection data of a single patient. This avoids having a bias toward a group of patients in the dataset and helps to capture unique characteristics of the individual properly. We validated the proposed method using fan-beam CT simulation data of an extended cardiac-torso phantom and compared the results with total variation-based iterative reconstruction and an image-based convolutional neural network.