Artifact Reduction in Undersampled 3D Cone-Beam CTs using a Hybrid 2D-3D CNN Framework

TL;DR

This paper introduces a hybrid 2D-3D deep learning framework that effectively reduces artifacts in undersampled 3D cone-beam CTs, improving image quality while maintaining computational efficiency.

Contribution

A novel hybrid deep learning approach combining 2D and 3D CNNs for artifact reduction in undersampled 3D CTs, balancing efficiency and volumetric consistency.

Findings

Significant reduction of artifacts in undersampled CT volumes.

Improved inter-slice consistency in coronal and sagittal views.

Low computational overhead compared to purely 3D models.

Abstract

Undersampled CT volumes minimize acquisition time and radiation exposure but introduce artifacts degrading image quality and diagnostic utility. Reducing these artifacts is critical for high-quality imaging. We propose a computationally efficient hybrid deep-learning framework that combines the strengths of 2D and 3D models. First, a 2D U-Net operates on individual slices of undersampled CT volumes to extract feature maps. These slice-wise feature maps are then stacked across the volume and used as input to a 3D decoder, which utilizes contextual information across slices to predict an artifact-free 3D CT volume. The proposed two-stage approach balances the computational efficiency of 2D processing with the volumetric consistency provided by 3D modeling. The results show substantial improvements in inter-slice consistency in coronal and sagittal direction with low computational overhead. This hybrid framework presents a robust and efficient solution for high-quality 3D CT image post-processing. The code of this project can be found on github: https://github.com/J-3TO/2D-3DCNN_sparseview/.