Cone Beam Computed Tomography (CBCT) plays a vital role in clinical imaging. Traditional methods typically require hundreds of 2D X-ray projections to reconstruct a high-quality 3D CBCT image, leading to considerable radiation exposure. This has led to a growing interest in sparse-view CBCT reconstruction to reduce radiation doses. While recent advances, including deep learning and neural rendering algorithms, have made strides in this area, these methods either produce unsatisfactory results or suffer from time inefficiency of individual optimization. In this paper, we introduce a novel geometry-aware encoder-decoder framework to solve this problem. Our framework starts by encoding multi-view 2D features from various 2D X-ray projections with a 2D CNN encoder. Leveraging the geometry of CBCT scanning, it then back-projects the multi-view 2D features into the 3D space to formulate a comprehensive volumetric feature map, followed by a 3D CNN decoder to recover 3D CBCT image. Importantly, our approach respects the geometric relationship between 3D CBCT image and its 2D X-ray projections during feature back projection stage, and enjoys the prior knowledge learned from the data population. This ensures its adaptability in dealing with extremly sparse view inputs without individual training, such as scenarios with only 5 or 10 X-ray projections. Extensive evaluations on two simulated datasets and one real-world dataset demonstrate exceptional reconstruction quality and time efficiency of our method.

翻译：锥束计算机断层扫描（CBCT）在临床成像中发挥着至关重要的作用。传统方法通常需要数百张二维X射线投影来重建高质量的3D CBCT图像，这会导致显著的辐射暴露。因此，为降低辐射剂量，稀疏视角CBCT重建日益受到关注。尽管包括深度学习和神经渲染算法在内的最新进展已在该领域取得进步，但这些方法要么产生不尽人意的结果，要么受限于个体优化的时间低效性。本文提出了一种新颖的几何感知编码器-解码器框架来解决此问题。我们的框架首先使用2D CNN编码器从多个二维X射线投影中编码多视角二维特征。利用CBCT扫描的几何特性，随后将多视角二维特征反投影至三维空间以构建全面的体素特征图，再通过3D CNN解码器重建3D CBCT图像。重要的是，我们的方法在特征反投影阶段遵循3D CBCT图像与其二维X射线投影之间的几何关系，并充分利用从数据群体中学到的先验知识。这确保了其在处理极稀疏视角输入（例如仅含5或10个X射线投影的场景）时无需单独训练的适应能力。在两个模拟数据集和一个真实数据集上的广泛评估表明，我们的方法具有卓越的重建质量和时间效率。