Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models

TL;DR

This paper introduces a new method to correct artifacts in Cone-Beam CT images using a diffusion model, resulting in clearer and more accurate images for medical use.

Contribution

The novel contribution is the development of ScDiff, an unsupervised adversarial diffusion model for CBCT artifact correction that outperforms existing methods.

Findings

ScDiff effectively reduces artifacts in CBCT images while preserving anatomical structures.

The proposed method achieves higher image quality and better evaluation metrics than existing GAN- and diffusion-based approaches.

Abstract

Cone-Beam Computed Tomography (CBCT) holds significant clinical value in image-guided radiotherapy (IGRT). However, CBCT images of low-density soft tissues are often plagued with artifacts and noise, which can lead to missed diagnoses and misdiagnoses. We propose a new unsupervised CBCT image artifact correction algorithm, named Spatial Convolution Diffusion (ScDiff), based on a conditional diffusion model, which combines the unsupervised learning ability of generative adaptive networks (GAN) with the stable training characteristics of diffusion models. This approach can efficiently and stably achieve CBCT image artifact correction, resulting in clear, realistic CBCT images with complete anatomical structures. The proposed model can effectively improve the image quality of CBCT. The obtained results can reduce artifacts while preserving the anatomical structure of CBCT images. We compared the proposed method with several GAN- and diffusion-based methods. Our method achieved the highest corrected image quality and the best evaluation metrics.

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