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SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields

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arxiv 2211.17048 v1 pith:VNFCMCV6 submitted 2022-11-30 eess.IV cs.CV

Yu Fang , Lanzhuju Mei , Changjian Li , Yuan Liu , Wenping Wang , Zhiming Cui , Dinggang Shen This is my paper · ORCID
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keywords cbctbeenreconstructionsparse-viewapproachattenuationclinicalfields
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. 4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images

    eess.IV 2024-12 conditional novelty 6.0 of 10

    4DRGS models vessels as 4D radiative Gaussians with a neural attenuation field and reconstructs 3D vessel volumes from sparse-view dynamic DSA images in about 5 to 13 minutes, matching or beating prior state-of-the-ar...

  2. Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Model

    cs.CV 2025-11 conditional novelty 5.0 of 10

    Diff-NAF iteratively synthesizes and diffusion-refines missing projection views, using them as pseudo-labels to progressively improve ultra-sparse-view CT reconstructions.

  3. Trans${^2}$-CBCT: A Dual-Transformer Framework for Sparse-View CBCT Reconstruction

    eess.IV 2025-06 conditional novelty 5.0 of 10

    Trans2-CBCT combines a TransUNet feature extractor with a neighbor-aware Point Transformer and reports state-of-the-art PSNR/SSIM for 6-10 view sparse-view CBCT reconstruction on LUNA16 and ToothFairy.

  4. ViT-NeBLa: A Hybrid Vision Transformer and Neural Beer-Lambert Framework for Single-View 3D Reconstruction of Oral Anatomy from Panoramic Radiographs

    eess.IV 2025-06 conditional novelty 4.0 of 10

    ViT-NeBLa reconstructs 3D oral anatomy from a single synthetic panoramic radiograph, reporting higher PSNR, SSIM, and LPIPS than three baselines on a private dataset, but it is not validated on real X-rays or against ...

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