用2D+3D混合模型高效修复低采样率3D CT图像伪影
Artifact Reduction in Undersampled 3D Cone-Beam CTs using a Hybrid 2D-3D CNN Framework
- 分两阶段：先2D U-Net处理每层切片，再3D解码器融合跨层信息
- 在冠状和矢状方向上显著提升切片间一致性，计算开销小
- 适合需要快速高质量重建的临床CT后处理场景
低采样率CT可减少扫描时间和辐射剂量，但会引入影响图像质量与诊断价值的伪影。本文提出一种计算高效的混合深度学习框架，结合2D与3D模型优势：首先用2D U-Net对低采样CT各切片进行特征提取；随后将所得特征图沿体积分层堆叠，输入3D解码器，利用跨切片上下文信息生成无伪影的3D CT图像。该两阶段方法在保持2D处理高效率的同时，实现3D体数据的一致性重建。实验表明，在冠状面和矢状面方向上，切片间一致性显著改善，且计算开销低。该混合框架为高质量3D CT图像后处理提供了鲁棒高效的解决方案。代码已开源：https://github.com/J-3TO/2D-3DCNN_sparseview/
原文摘要 · Abstract (English)
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/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。