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.
翻译:在临床实践中,特别是在牙科诊所,广泛使用Cone光束计算断层摄影(CBCT),而采集时X光的辐射剂量一直是CBCT成像中长期关注的问题,已提出若干研究工作,从稀有的2D预测中重建高质量的CBCT图像,但目前艺术的状态受到艺术品的破坏,缺乏详细细节。在本文件中,我们建议SNAF通过学习神经衰减领域,进行稀薄的视野CBCT重建,我们在这方面发明了一种新颖的增强观点战略,以克服投入量不足的数据所带来的挑战。我们的方法在高重建质量(30+ PSNR)方面取得了优异性业绩,只有20个投入量(比临床采集少25倍),这比现有艺术的状态要差。我们进一步进行了全面试验和对比分析,以验证我们的方法的有效性。