| 基于深度学习的医用锥形束CT图像散射伪影校正的研究进展 |
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| 引用本文： | 张文君,黄钢,丁海宁,徐红春.基于深度学习的医用锥形束CT图像散射伪影校正的研究进展[J].CT理论与应用研究,2023,32(2):285-296. |
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| 作者姓名： | 张文君 黄钢 丁海宁 徐红春 |
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| 作者单位： | 1.上海理工大学健康科学与工程学院, 上海200093 |
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| 基金项目： | 国家自然科学基金（整环SPECT/能谱CT一体化分子影像仪的研发（82127807））；上海市分子影像学重点实验室建设项目（批准号:18DZ2260400）。 |
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| 摘 要： | 医用计算机断层扫描成像系统中，X射线与物体相互作用产生的康普顿散射光子严重影响了图像质量，尤其在锥形束计算机断层扫描和多层探测器系统中。目前已有许多散射伪影校正方法，归纳为3类:硬件校正、软件校正、软硬件混合校正方法，但近年随着计算机计算能力的提高以及深度学习在医学图像处理领域的发展，出现了一些新的散射校正方法。本文首先介绍传统校正方法；然后详细介绍基于深度学习方法进行散射伪影校正，并将其分为基于图像域和基于投影域的深度学习方法，以及对不同的深度学习网络在散射伪影校正中的应用进行讨论；最后展望深度学习在多源计算机断层扫描技术中的应用前景。
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| 关 键 词： | X射线光学 深度学习 康普顿散射 锥形束CT 伪影校正 |
| 收稿时间： | 2022-06-29 |
Research Progress of Scattering Artifact Correction in Medical Cone-beam Computed Tomography Imaging Based on Deep Learning |
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| Institution: | 1.School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China2.Shanghai Key Laboratory of Molecular Imaging, Jiading District Central Hospital, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China3.Nano Vision (Shanghai) Medical Technology Company Limited, Shanghai 200120, China |
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| Abstract: | In medical computed tomography imaging systems, Compton scattered photons generated by the interaction between X-rays and objects have a serious impact on image quality, especially in cone-beam computed tomography and multi-layer detector systems. Currently, there are many scattering artifact correction methods, which can be classified into three categories: hardware, software, and hybrid software and hardware correction methods. However, with the advances in computing power and development of deep learning in medical image processing, new methods of scattering artifact correction have appeared in recent years. This study first introduces traditional correction methods. Then, a method of scattering artifact correction based on deep learning is described in detail, which is divided into the correction method based on image domain and the correction method based on projection domain. Various deep-learning neural networks for this method are also introduced in detail. Finally, the application prospects of the deep learning method in multi-source computed tomography imaging scattering artifacts were probed . |
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