Abstract
Cone-beam computed tomography (CBCT) is a widely used 3D imaging technique in dentistry, offering high-resolution images while minimising radiation exposure for patients. However, CBCT is highly susceptible to artefacts arising from high-density objects such as dental implants, which can compromise image quality and diagnostic accuracy. To reduce artefacts, implant inpainting in the sequence of projections plays a crucial role in many artefact reduction approaches. Recently, diffusion models have achieved state-of-the-art results in image generation and have widely been applied to image inpainting tasks. However, to our knowledge, existing diffusion-based methods for implant inpainting operate on independent 2D projections. This approach neglects the correlations among individual projections, resulting in inconsistencies in the reconstructed images. To address this, we propose a 3D dental implant inpainting approach based on perpendicular score-based diffusion models, each trained in two different planes and operating in the projection domain. The 3D distribution of the projection series is modelled by combining the two 2D score-based diffusion models in the sampling scheme. Our results demonstrate the method's effectiveness in producing high-quality, artefact-reduced 3D CBCT images, making it a promising solution for improving clinical imaging.
Abstract (translated)
锥束计算机断层扫描（CBCT）是牙科中广泛使用的三维成像技术，它能提供高分辨率的图像同时将患者的辐射暴露降至最低。然而，CBCT极易受到诸如牙齿植入物等高密度物体引起的伪影影响，这会降低图像质量和诊断准确性。为了减少这些伪影，序列投影中的植入体修复在许多伪影减少方法中扮演着关键角色。最近，扩散模型在图像生成方面取得了最先进的成果，并被广泛应用于图像修复任务中。然而，据我们所知，现有的基于扩散的方法对于植入体修复仅操作于独立的二维投影上。这种方法忽视了各个投影之间的关联性，在重建图像时会产生不一致性。为了解决这一问题，我们提出了一种基于垂直评分扩散模型的三维牙科植入体修复方法，该方法在两个不同的平面上进行训练，并且是在投影域内操作的。通过结合这两个二维评分扩散模型来构建投影序列的三维分布，在采样方案中实现了这一点。我们的结果显示，这种方法能够有效生成高质量、伪影减少的3D CBCT图像，有望成为改善临床成像的一个有前景的解决方案。
URL
https://arxiv.org/abs/2603.06300
