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arXiv · 2211.17048

SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields

Yu Fang·Lanzhuju Mei·Changjian Li·Yuan Liu·Wenping Wang·Zhiming Cui·Dinggang Shen
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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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Yu Fang, Lanzhuju Mei, Changjian Li, Yuan Liu, Wenping Wang, Zhiming Cui, Dinggang Shen. 2022-11-30. SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields. https://arxiv.org/abs/2211.17048

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