Texture-preserving diffusion model for CBCT-to-CT synthesis

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Medical image analysis Pub Date : 2024-10-09 DOI:10.1016/j.media.2024.103362
Youjian Zhang , Li Li , Jie Wang , Xinquan Yang , Haotian Zhou , Jiahui He , Yaoqin Xie , Yuming Jiang , Wei Sun , Xinyuan Zhang , Guanqun Zhou , Zhicheng Zhang
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Abstract

Cone beam computed tomography (CBCT) serves as a vital imaging modality in diverse clinical applications, but is constrained by inherent limitations such as reduced image quality and increased noise. In contrast, computed tomography (CT) offers superior resolution and tissue contrast. Bridging the gap between these modalities through CBCT-to-CT synthesis becomes imperative. Deep learning techniques have enhanced this synthesis, yet challenges with generative adversarial networks persist. Denoising Diffusion Probabilistic Models have emerged as a promising alternative in image synthesis. In this study, we propose a novel texture-preserving diffusion model for CBCT-to-CT synthesis that incorporates adaptive high-frequency optimization and a dual-mode feature fusion module. Our method aims to enhance high-frequency details, effectively fuse cross-modality features, and preserve fine image structures. Extensive validation demonstrates superior performance over existing methods, showcasing better generalization. The proposed model offers a transformative pathway to augment diagnostic accuracy and refine treatment planning across various clinical settings. This work represents a pivotal step toward non-invasive, safer, and high-quality CBCT-to-CT synthesis, advancing personalized diagnostic imaging practices.
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用于 CBCT-to-CT 合成的纹理保护扩散模型。
锥形束计算机断层扫描(CBCT)是多种临床应用中的重要成像模式,但受限于其固有的局限性,如图像质量下降和噪声增加。相比之下,计算机断层扫描(CT)具有更高的分辨率和组织对比度。通过 CBCT 到 CT 的合成来缩小这些模式之间的差距势在必行。深度学习技术增强了这种合成,但生成对抗网络的挑战依然存在。去噪扩散概率模型已成为图像合成中一种有前途的替代方法。在本研究中,我们提出了一种用于 CBCT 到 CT 合成的新型纹理保护扩散模型,该模型结合了自适应高频优化和双模式特征融合模块。我们的方法旨在增强高频细节、有效融合跨模态特征并保留精细图像结构。广泛的验证表明,与现有方法相比,我们的方法性能更优,通用性更好。所提出的模型为在各种临床环境中提高诊断准确性和完善治疗计划提供了一条变革之路。这项工作代表着向无创、更安全、高质量的 CBCT 到 CT 合成迈出了关键一步,推动了个性化成像诊断实践的发展。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
期刊最新文献
Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333]. Editorial for Special Issue on Foundation Models for Medical Image Analysis. Few-shot medical image segmentation with high-fidelity prototypes. The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction. AutoFOX: An automated cross-modal 3D fusion framework of coronary X-ray angiography and OCT.
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