3D CBCT Challenge 2024：使用基于 SwinIR 的正弦图和图像增强改进锥束 CT 重建
3D CBCT Challenge 2024: Improved Cone Beam CT Reconstruction using SwinIR-Based Sinogram and Image Enhancement
AI总结：
本文提出一种集成基于 SwinIR 的正弦图与图像增强模块，并采用 NAG-LS 求解 CT 重建的 CBCT 重建方法，显著降低了低剂量和临床剂量下的 MSE，位列挑战赛前五。
摘要：
本文介绍了我们针对 3D CBCT Challenge 2024（ICASSP SP Grand Challenges 2024 的一部分）的方法。通过集成基于 SwinIR 的正弦图和图像增强模块，实现了锥束 CT（CBCT）重建的改进。该方法使用 Nesterov 加速梯度下降（NAG）求解 CT 图像重建中的最小二乘（NAG-LS）问题。正弦图和图像增强模块的集成旨在提升图像清晰度并保留精细细节，为低剂量和临床剂量 CBCT 重建提供了有前景的解决方案。验证数据集上的平均均方误差（MSE）显著下降，低剂量情况下降五分之一，临床剂量下降十分之一。我们的解决方案是该挑战赛的前 5 名方法之一。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
