Sparse-view cone-beam CT (CBCT) reconstruction is an important direction to reduce radiation dose and benefit clinical applications. Previous voxel-based generation methods represent the CT as discrete voxels, resulting in high memory requirements and limited spatial resolution due to the use of 3D decoders. In this paper, we formulate the CT volume as a continuous intensity field and develop a novel DIF-Net to perform high-quality CBCT reconstruction from extremely sparse (fewer than 10) projection views at an ultrafast speed. The intensity field of a CT can be regarded as a continuous function of 3D spatial points. Therefore, the reconstruction can be reformulated as regressing the intensity value of an arbitrary 3D point from given sparse projections. Specifically, for a point, DIF-Net extracts its view-specific features from different 2D projection views. These features are subsequently aggregated by a fusion module for intensity estimation. Notably, thousands of points can be processed in parallel to improve efficiency during training and testing. In practice, we collect a knee CBCT dataset to train and evaluate DIF-Net. Extensive experiments show that our approach can reconstruct CBCT with high image quality and high spatial resolution from extremely sparse views within 1.6 seconds, significantly outperforming state-of-the-art methods. Our code will be available at https://github.com/xmed-lab/DIF-Net.

翻译：稀疏视角锥束CT（CBCT）重建是降低辐射剂量并促进临床应用的重要方向。以往的基于体素的生成方法将CT表示为离散体素，由于使用3D解码器，导致内存需求高且空间分辨率受限。本文中，我们将CT体积表示为连续强度场，并开发了一种新颖的DIF-Net，以超快速度从极稀疏（少于10个）投影视角实现高质量CBCT重建。CT的强度场可视为三维空间点的连续函数。因此，重建可重新定义为根据给定的稀疏投影回归任意三维点的强度值。具体而言，对于某一点，DIF-Net从不同二维投影视角提取其视角特定特征，随后通过融合模块聚合这些特征以估计强度值。值得注意的是，在训练和测试过程中，可并行处理数千个点以提高效率。实践中，我们收集了膝关节CBCT数据集来训练和评估DIF-Net。大量实验表明，我们的方法能在1.6秒内从极稀疏视角重建出具有高图像质量和高空间分辨率的CBCT，显著优于现有最先进方法。我们的代码将发布在https://github.com/xmed-lab/DIF-Net。