Residual Neural Network for Filter Kernel Design in Filtered Back-projection for CT Image Reconstruction

Abstract

Filtered back-projection (FBP) has been widely applied for computed tomography (CT) image reconstruction as a fundamental algorithm. Most of the filter kernels used in FBP are designed by analytic methods. Recently, the precision learning-based ramp filter (PL-Ramp) has been proposed to formulate FBP to directly learn the reconstruction filter. However, it is difficult to introduce regularization terms in this method, which essentially provides a massive solution space. Therefore, in this paper, we propose a neural network based on residual learning for filter kernel design in FBP, named resFBP. With such a neural network, it is possible for us to limit the solution space by introducing various regularization terms or methods to achieve better reconstruction quality on the test set. The experiment results demonstrate that both quality and reconstruction error of the proposed method has great superiority over FBP and also outperforms PL-Ramp when projection data are polluted by Poisson noise or Gaussian noise.