3D CBCT Challenge 2024: Improved Cone Beam CT Reconstruction using SwinIR-Based Sinogram and Image Enhancement

TL;DR

This paper introduces a novel CBCT reconstruction method utilizing SwinIR-based modules and Nesterov Accelerated Gradient Descent, significantly improving image quality for low and clinical dose scans in the 3D CBCT Challenge 2024.

Contribution

It presents a new approach combining SwinIR modules with NAG optimization for enhanced CBCT reconstruction, achieving state-of-the-art results.

Findings

MSE reduced by 80% for low dose CBCT

MSE reduced by 90% for clinical dose CBCT

Ranked among top 5 solutions in the challenge

Abstract

In this paper, we present our approach to the 3D CBCT Challenge 2024, a part of ICASSP SP Grand Challenges 2024. Improvement in Cone Beam Computed Tomography (CBCT) reconstruction has been achieved by integrating Swin Image Restoration (SwinIR) based sinogram and image enhancement modules. The proposed methodology uses Nesterov Accelerated Gradient Descent (NAG) to solve the least squares (NAG-LS) problem in CT image reconstruction. The integration of sinogram and image enhancement modules aims to enhance image clarity and preserve fine details, offering a promising solution for both low dose and clinical dose CBCT reconstruction. The averaged mean squared error (MSE) over the validation dataset has decreased significantly, in the case of low dose by one-fifth and clinical dose by one-tenth. Our solution is one of the top 5 approaches in this challenge.