Bayesian ensemble learning with denoiser pool for low-dose CT reconstruction

Abstract

Deep learning (DL)-based algorithms have shown promising performance in low-dose computed tomography (LDCT) and are becoming mainstream methods. These DL-based methods focus on different aspects of CT image restoration, such as noise suppression, artifacts removal, structure preservation, etc. Therefore, in this paper, we propose a bayesian ensemble learning network (BENet) that fuses several representative denoising algorithms from the denoiser pool to improve the LDCT imaging performance. Specifically, we first select four advanced CT image and natural image restoration networks, including REDCNN, FBPConvNet, HINet, and Restormer, to form the denoiser pool, which integrates the denoising capabilities of different networks. The denoiser pool is pre-trained to obtain the denoising results of each denoiser. Then, we present a bayesian neural network to predict the weight maps and variances of denoiser pool by modeling the aleatoric and epistemic uncertainties of DL. Finally, the predicted pixel-wise weight maps are used to fuse the denoising results to obtain the final reconstruction result. Qualitative and quantitative analysis results have shown that the proposed BENet can effectively boost the denoising performance and robustness of LDCT image reconstruction.