SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields

• URL: http://arxiv.org/abs/2211.17048v1
• Date: Wed, 30 Nov 2022 14:51:14 GMT
• Title: SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields
• Authors: Yu Fang, Lanzhuju Mei, Changjian Li, Yuan Liu, Wenping Wang, Zhiming Cui, Dinggang Shen
• Abstract summary: We propose SNAF for sparse-view CBCT reconstruction by learning the neural attenuation fields. Our approach achieves superior performance in terms of high reconstruction quality (30+ PSNR) with only 20 input views.
• Score: 71.84366290195487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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