SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields

Yu Fang, Lanzhuju Mei, Changjian Li, Yuan Liu, Wenping Wang, Zhiming Cui, Dinggang Shen

Research output: Working paperPreprint

Abstract / Description of output

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.
Original languageEnglish
Number of pages9
Publication statusPublished - 30 Nov 2022

Keywords / Materials (for Non-textual outputs)

  • Image and Video Processing (eess.IV)
  • Computer Vision and Pattern Recognition (cs.CV)
  • FOS: Electrical engineering
  • electronic engineering
  • information engineering


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