SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields
This addresses radiation dose concerns in clinical CBCT imaging by enabling high-quality reconstructions from sparse data, though it appears incremental as it builds on existing neural field approaches.
The paper tackles sparse-view CBCT reconstruction to reduce radiation dose in dental clinics, achieving high-quality results with 30+ PSNR using only 20 input views, which outperforms state-of-the-art methods.
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.