3D CBCT Artefact Removal Using Perpendicular Score-Based Diffusion Models

• URL: http://arxiv.org/abs/2603.06300v1
• Date: Fri, 06 Mar 2026 14:04:04 GMT
• Title: 3D CBCT Artefact Removal Using Perpendicular Score-Based Diffusion Models
• Abstract summary: We propose a 3D dental implant inpainting approach based on perpendicular score-based diffusion models.<n>Our results demonstrate the method's effectiveness in producing high-quality, artefact-reduced 3D CBCT images.
• Score: 0.5968063252533801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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