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

Mingqiang Meng; Yongbo Wang; Manman Zhu; Zhaoying Bian; Dong Zeng; Jianhua Ma

doi:10.1117/12.2654202

7 April 2023 Bayesian ensemble learning with denoiser pool for low-dose CT reconstruction

Mingqiang Meng, Yongbo Wang, Manman Zhu, Zhaoying Bian, Dong Zeng, Jianhua Ma

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124633D (2023) https://doi.org/10.1117/12.2654202
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

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.

Citation Download Citation

Mingqiang Meng, Yongbo Wang, Manman Zhu, Zhaoying Bian, Dong Zeng, and Jianhua Ma "Bayesian ensemble learning with denoiser pool for low-dose CT reconstruction", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124633D (7 April 2023); https://doi.org/10.1117/12.2654202

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available