使用垂直分数扩散模型进行3D锥束CT伪影去除
3D CBCT Artefact Removal Using Perpendicular Score-Based Diffusion Models
AI总结：
本文提出基于垂直分数扩散模型的3D牙科植入物修复方法，通过结合两个2D模型在投影域中操作，有效减少CBCT图像伪影，提升临床成像质量。
摘要：
锥束计算层析成像（CBCT）是一种在牙科中广泛使用的3D成像技术，能够提供高分辨率图像同时最小化患者辐射暴露。然而，CBCT对高密度物体（如牙科植入物）引起的伪影非常敏感，这会损害图像质量和诊断准确性。为减少伪影，序列投影中的植入物修复在许多伪影减少方法中起着关键作用。最近，扩散模型在图像生成中取得了最先进的成果，并广泛应用于图像修复任务。然而，到目前为止，现有的基于扩散的方法用于植入物修复是在独立的2D投影上进行的。这种方法忽略了单个投影之间的相关性，导致重建图像的一致性问题。为了解决这个问题，我们提出了一种基于垂直分数扩散模型的3D牙科植入物修复方法，每个模型在两个不同的平面进行训练并在投影域中运行。通过将两个2D分数扩散模型结合在采样方案中，对投影系列的3D分布进行建模。我们的结果表明，该方法在生成高质量、伪影减少的3D CBCT图像方面是有效的，使其成为改进临床成像的一种有前途的解决方案。
英文摘要：
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.
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