Cone-Beam Computed Tomography (CBCT) is an indispensable technique in medical imaging, yet the associated radiation exposure raises concerns in clinical practice. To mitigate these risks, sparse-view reconstruction has emerged as an essential research direction, aiming to reduce the radiation dose by utilizing fewer projections for CT reconstruction. Although implicit neural representations have been introduced for sparse-view CBCT reconstruction, existing methods primarily focus on local 2D features queried from sparse projections, which is insufficient to process the more complicated anatomical structures, such as the chest. To this end, we propose a novel reconstruction framework, namely DIF-Gaussian, which leverages 3D Gaussians to represent the feature distribution in the 3D space, offering additional 3D spatial information to facilitate the estimation of attenuation coefficients. Furthermore, we incorporate test-time optimization during inference to further improve the generalization capability of the model. We evaluate DIF-Gaussian on two public datasets, showing significantly superior reconstruction performance than previous state-of-the-art methods.
锥束计算机断层扫描（CBCT）在医学成像中是一种不可或缺的技术，但相关的辐射暴露引起临床实践中的担忧。为了减少这些风险，稀疏视角重建已经成为一个重要的研究方向，旨在通过利用更少的投影来减少CT重建的辐射剂量。尽管隐式神经表示已经被引入用于稀疏视角CBCT重建，现有方法主要集中在从稀疏投影中查询的局部2D特征上，这对于处理胸部等更复杂的解剖结构是不足够的。 为此，我们提出了一种新的重建框架，名为DIF-Gaussian，它利用三维高斯函数来表示三维空间中的特征分布，提供额外的三维空间信息以便于估计衰减系数。此外，我们在推理过程中引入了测试时优化，进一步提高模型的泛化能力。我们在两个公开数据集上评估了DIF-Gaussian，在重建性能上显示出显著优于先前最先进方法的结果。