用2D X光片生成3D CT，低辐射下实现高分辨率成像。
DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or Mono-planar 2D X-ray(s)
- 通过新Transformer融合2D X光与3D位置信息作为条件。
- 在双平面或单平面数据上重建效果优于现有方法。
- 适合医学影像低剂量成像研究者使用。
计算机断层扫描（CT）可提供高分辨率医学影像，但患者暴露于高辐射；而X光扫描辐射低，但分辨率差。本文提出一种新型条件扩散模型DX2CT，可从双平面或单平面2D X光图像重建三维（3D）CT体积。该模型包含两个关键组件：1）利用新型Transformer将2D X光提取的特征图与3D CT体素位置进行调制；2）有效利用调制后的3D位置感知特征图作为条件输入。该Transformer能为条件扩散模型提供目标CT切片的丰富信息，从而实现高质量重建。在双平面或单平面X光基准数据集上的实验表明，所提方法优于多个先进方法。代码与模型将开源于：https://www.github.com/intyeger/DX2CT。
原文摘要 · Abstract (English)
Computational tomography (CT) provides high-resolution medical imaging, but it can expose patients to high radiation. X-ray scanners have low radiation exposure, but their resolutions are low. This paper proposes a new conditional diffusion model, DX2CT, that reconstructs three-dimensional (3D) CT volumes from bi or mono-planar X-ray image(s). Proposed DX2CT consists of two key components: 1) modulating feature maps extracted from two-dimensional (2D) X-ray(s) with 3D positions of CT volume using a new transformer and 2) effectively using the modulated 3D position-aware feature maps as conditions of DX2CT. In particular, the proposed transformer can provide conditions with rich information of a target CT slice to the conditional diffusion model, enabling high-quality CT reconstruction. Our experiments with the bi or mono-planar X-ray(s) benchmark datasets show that proposed DX2CT outperforms several state-of-the-art methods. Our codes and model will be available at: https://www.github.com/intyeger/DX2CT.
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。