A dual-domain network with division residual connection and feature fusion for CBCT scatter correction

Shuo Yang; Zhe Wang; Linjie Chen; Ying Cheng; Huamin Wang; Xiao Bai; Guohua Cao

doi:10.1088/1361-6560/adaf06

A dual-domain network with division residual connection and feature fusion for CBCT scatter correction

Phys Med Biol. 2025 Jan 27. doi: 10.1088/1361-6560/adaf06. Online ahead of print.

Authors

Shuo Yang¹, Zhe Wang², Linjie Chen¹, Ying Cheng¹, Huamin Wang¹, Xiao Bai¹, Guohua Cao¹

Affiliations

¹ School of Biomedical Engineering, ShanghaiTech University, No. 1 Zhongke Road, Pudong New Area, Shanghai, Shanghai, 201210, CHINA.
² School of Biomedical Engineering, ShanghaiTech University, No. 1 Zhongke Road, Pudong New Area, Shanghai, 201210, CHINA.

PMID: 39870035
DOI: 10.1088/1361-6560/adaf06

Abstract

Objective: This study aims to propose a dual-domain network that not only reduces scatter artifacts but also retains structure details in CBCT.

Approach: The proposed network comprises a projection-domain sub-network and an image-domain sub-network. The projection-domain sub-network utilizes a division residual network to amplify the difference between scatter signals and imaging signals, facilitating the learning of scatter signals. The image-domain sub-network contains dual encoders and a single decoder. The dual encoders extract features from two inputs parallelly, and the decoder fuses the extracted features from the two encoders and maps the fused features back to the final high-quality image. Of the two input images to the image-domain sub-network, one is the scatter-contaminated image analytically reconstructed from the scatter-contaminated projections, and the other is the pre-processed image reconstructed from the pre-processed projections produced by the projection-domain sub-network.

Main results: Experimental results on both synthetic and real data demonstrate that our method can effectively reduce scatter artifacts and restore image details. Quantitative analysis using synthetic data shows the mean absolute error (MAE) was reduced by 74% and peak signal-to-noise ratio (PSNR) increased by 57% compared to the scatter-contaminated ones. Testing on real data found a 38% increase in contrast-to-noise ratio (CNR) with our method compared to the scatter-contaminated image. Additionally, our method consistently outperforms comparative methods such as U-Net, DSE-Net, RDCNN and the collimator-based method.

Significance: A dual-domain network that leverages projection-domain division residual connection and image-domain feature fusion has been proposed for CBCT scatter correction. It has potential applications for reducing scatter artifacts and preserving image details in CBCT.

Keywords: Cone-beam CT; Deep learning; Image processing; Scatter correction; Tomographic reconstruction.