论文标题
SNAF：与神经衰减场的稀疏视图CBCT重建
SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields
论文作者
论文摘要
锥束计算机断层扫描（CBCT）已被广泛用于临床实践，尤其是在牙科诊所中，而捕获时X射线的辐射剂量一直是CBCT成像的长期关注。已经提出了几项研究工作，以重建稀疏视图2D预测的高质量CBCT图像，但是当前的最新艺术品遭受了文物的影响和缺乏细节。在本文中，我们通过学习神经衰减领域提出了SNAF进行稀疏视图CBCT重建，在那里我们发明了一种新颖的视图增强策略来克服从稀疏输入视图中数据不足的挑战。我们的方法在高重建质量（30+ psnr）方面具有出色的性能，只有20个输入视图（比临床收集少25倍），这表现优于最先进的。我们进一步进行了全面的实验和消融分析，以验证我们方法的有效性。
Cone beam computed tomography (CBCT) has been widely used in clinical practice, especially in dental clinics, while the radiation dose of X-rays when capturing has been a long concern in CBCT imaging. Several research works have been proposed to reconstruct high-quality CBCT images from sparse-view 2D projections, but the current state-of-the-arts suffer from artifacts and the lack of fine details. In this paper, we propose SNAF for sparse-view CBCT reconstruction by learning the neural attenuation fields, where we have invented a novel view augmentation strategy to overcome the challenges introduced by insufficient data from sparse input views. Our approach achieves superior performance in terms of high reconstruction quality (30+ PSNR) with only 20 input views (25 times fewer than clinical collections), which outperforms the state-of-the-arts. We have further conducted comprehensive experiments and ablation analysis to validate the effectiveness of our approach.
