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双能CT图像域基材料分解算法的研究进展

郭俏 姚旭峰

郭俏, 姚旭峰. 双能CT图像域基材料分解算法的研究进展[J]. CT理论与应用研究, 2023, 32(1): 1-8. DOI: 10.15953/j.ctta.2021.067
引用本文: 郭俏, 姚旭峰. 双能CT图像域基材料分解算法的研究进展[J]. CT理论与应用研究, 2023, 32(1): 1-8. DOI: 10.15953/j.ctta.2021.067
GUO Q, YAO X F. Progress of Material Decomposition Algorithms in Dual-energy CT Imaging[J]. CT Theory and Applications, 2023, 32(1): 1-8. DOI: 10.15953/j.ctta.2021.067. (in Chinese)
Citation: GUO Q, YAO X F. Progress of Material Decomposition Algorithms in Dual-energy CT Imaging[J]. CT Theory and Applications, 2023, 32(1): 1-8. DOI: 10.15953/j.ctta.2021.067. (in Chinese)

双能CT图像域基材料分解算法的研究进展

doi: 10.15953/j.ctta.2021.067
基金项目: 科技部国家重点研发计划(基于区块链的老年主动健康智能照护平台研究与应用示范(2020YFC2008700));国家自然科学基金面上项目(基于智能影像组学技术的阿尔兹海默病早期预测方法研究(61971275));国家自然科学基金重点项目(代谢影像组学智能预测肺癌靶向耐药的关键技术与应用(81830052))。
详细信息
    作者简介:

    郭俏:女,上海理工大学电子信息专业在读硕士研究生,主要从事医学图像处理方面的研究,E-mail:919806842@qq.com

    姚旭峰:男,上海健康医学院医学影像学院教授、博士生导师,主要从事医学图像处理方面的研究,E-mail:yao6636329@hotmail.com

  • 中图分类号: R  812;R  318

Progress of Material Decomposition Algorithms in Dual-energy CT Imaging

More Information
    Corresponding author: 男,上海健康医学院医学影像学院教授、博士生导师,主要从事医学图像处理方面的研究,E-mail:yao6636329@hotmail.com
  • 摘要: 能谱CT可产生不同X射线能量下的基材料图像,所产生的基材料图像可用于组织成分和造影剂分布的定性与定量评价,且对成像物质分离、鉴别的能力明显优于传统单能CT。能谱CT中双能谱技术是最常用的模式之一,在临床应用中发挥了重大作用。本文就双能谱CT图像域基材料分解的两物质分解、多物质分解方法进行总结,最后展望未来可能的发展方向。

     

  • 图  1  两种分解方法的模拟复现结果

    (a)和(b)是直接矩阵求逆法分解的骨和软组织的基材料图像;(c)和(d)是迭代分解法分解的骨和软组织的基材料图像。

    Figure  1.  Simulation results obtained with the two decomposition methods

    表  1  两种解决方案的主要研究

    Table  1.   Main research of two solutions

    方法文献时间优点缺点
    分解后去噪[13]1976 方法实现简单,计算效率高由于图像分辨率损失很大,效果有限
    [14]1984 实现简便效果有限
    [15]1985 实现简单效果有限
    [16]1988 缓解了空间分辨率损失的问题有边缘伪影
    [17]1995 算法可以在不考虑噪声相关性的情况下实现
     噪声抑制
    分解后的图像中高频噪声被过度抑制,导致图像纹理的改变
    [18]2003 算法利用CT或分解图像的冗余结构或统计信
     息进行噪声抑制,可以更好地抑制噪声
    没有完全描述DECT图像和分解图像之间的映射关系
    分解前去噪[19]2014 使重建的两幅CT图像噪声变得强烈相关,进
     而使得分解图像的噪声得到显著抑制
    CT重建和图像分解的结合增加了计算的复杂性,并且算法需要大量迭代才能收敛
    [20]2015 可以在保留定量测量和高频边缘信息的同
     时显著降低噪声
    在心肌成像中仍会存在边缘效应
    [21]2018 在抑制噪声的同时可以保持图像边缘细节没有考虑分解过程的噪声对图像的影响
    [22]2019 可获得高质量的重建CT图像以便后续分解没有考虑分解过程的噪声对图像的影响
    下载: 导出CSV

    表  2  基于深度学习方法分解图像的主要研究

    Table  2.   Main research of image decomposition based on deep learning method

    文献时间网络模型优点缺点
    [31]2018.03 U-Net分解图像有较低的噪声水平方法没有降噪能力,只具备分解能力
    [27]2018.09 FCN+FCL具有高分解精度和噪声鲁棒性在边缘保持方面没有明显改进
    [32]2019.04 蝴蝶网极大程度上抑制了图像噪声,提高了分解图像的质量如果改变X射线源设置,则需要重新
    训练网络
    [33]2021.03 DIWGAN分解图像与真实分解图像很接近,并且在噪声和伪影抑制方面效果较好在分解的软组织图像中有一些软组织
    结构丢失
    下载: 导出CSV
  • [1] 高海英. 能谱CT成像关键参数检测技术研究[D]. 广州: 南方医科大学, 2015.

    GAO H Y. Study on testing techniques of spectral CT imaging key parameters[D]. Guangzhou: Southern Medical University, 2015. (in Chinese).
    [2] 韩文艳. CT能谱成像的基本原理与临床应用优势[J]. 中国医疗设备, 2015,30(12): 90−91. doi: 10.3969/j.issn.1674-1633.2015.12.025

    HAN W Y. Basic principle and advantages of clinical application of CT energy spectrum imaging[J]. China Medical Devices, 2015, 30(12): 90−91. (in Chinese). doi: 10.3969/j.issn.1674-1633.2015.12.025
    [3] ALVAREZ R E, MACOVSKI A. Energy-selective reconstructions in X-ray computerized tomography[J]. Physics in Medicine & Biology, 1976, 21(5): 733−744.
    [4] 王丽新, 孙丰荣, 仲海, 等. 双能CT成像的数值仿真[J]. 航天医学与医学工程, 2015,28(5): 350−357.

    WANG L X, SUN F R, ZHONG H, et al. Numerical simulation of dual-energy CT imaging[J]. Space Medicine & Medical Engineering, 2015, 28(5): 350−357. (in Chinese).
    [5] MAASS C, BAER M, KACHELRIESS M. Image-based dual energy CT using optimized precorrection functions: A practical new approach of material decomposition in image domain[J]. Medical Physics, 2009, 36(8): 3818−3829. doi: 10.1118/1.3157235
    [6] 贺芳芳. 能谱CT基物质分解技术应用研究[D]. 济南: 山东大学, 2020.

    HE F F. Application research on basis material decomposition of spectral CT[D]. Jinan: Shandong University, 2020. (in Chinese).
    [7] MENDONÇA P R S, BHOTIKA R, MADDAH M, et al. Multi-material decomposition of spectral CT images[C]//Medical Imaging 2010: Physics of Medical Imaging. SPIE, 2010, 7622: 633-641.
    [8] 周正东, 章栩苓, 辛润超, 等. 基于MAP-EM算法的双能CT直接迭代基材料分解方法[J]. 东南大学学报(自然科学版), 2020,50(5): 935−941. doi: 10.3969/j.issn.1001-0505.2020.05.020

    ZHOU Z D, ZHANG X L, XIN R C, et al. Direct iterative basis material decomposition method for dual-energy CT based on MAP-EM algorithm[J]. Journal of Southeast University (Natural Science Edition), 2020, 50(5): 935−941. (in Chinese). doi: 10.3969/j.issn.1001-0505.2020.05.020
    [9] 孙英博, 孔慧华, 张雁霞. 基于投影域分解的多能谱CT造影剂物质识别研究[J]. 中北大学学报 (自然科学版), 2019,40(2): 167−172.

    SUN Y B, KONG H H, ZHANG Y X. Multi-energy spectral CT contrast agent material recognition based on projection domain decomposition[J]. Journal of North University of China (Natural Science Edition), 2019, 40(2): 167−172. (in Chinese).
    [10] LI Z, RAVISHANKAR S, LONG Y, et al. Learned mixed material models for efficient clustering based dual-energy CT image decomposition[C]//2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018: 529-533.
    [11] 李磊. 双能CT图像重建算法研究[D]. 郑州: 解放军信息工程大学, 2016.

    LI L. Research on image reconstruction algorithms of dual energy computed tomography[D]. Zhengzhou: Information Engineering University, 2016. (in Chinese).
    [12] MCCOLLOUGH C H, SCHMIDT B, LIU X, et al. Dual-energy algorithms and postprocessing techniques[M]//Dual energy CT in clinical practice. Springer, Berlin, Heidelberg, 2011: 43-51.
    [13] RUTHERFORD R, PULLAN B, ISHERWOOD I. X-ray energies for effective atomic number determination[J]. Neuroradiology, 1976, 11(1): 23−28. doi: 10.1007/BF00327254
    [14] NISHIMURA D G, MACOVSKI A, BRODY W R. Noise reduction methods for hybrid subtraction[J]. Medical Physics, 1984, 11(3): 259−265. doi: 10.1118/1.595501
    [15] JOHNS P C, YAFFE M J. Theoretical optimization of dual-energy X-ray imaging with application to mammography[J]. Medical Physics, 1985, 12(3): 289−296. doi: 10.1118/1.595766
    [16] KALENDER W A, KLOTZ E, KOSTARIDOU L. An algorithm for noise suppression in dual energy CT material density images[J]. IEEE Transactions on Medical Imaging, 1988, 7(3): 218−224. doi: 10.1109/42.7785
    [17] HINSHAW D A, DOBBINS III J T. Recent progress in noise reduction and scatter correction in dual-energy imaging[C]//Medical Imaging 1995: Physics of Medical Imaging, SPIE, 1995, 2432: 134-142.
    [18] WARP R J, DOBBINS J T. Quantitative evaluation of noise reduction strategies in dual-energy imaging[J]. Medical Physics, 2003, 30(2): 190−198. doi: 10.1118/1.1538232
    [19] DONG X, NIU T, ZHU L. Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization[J]. Medical Physics, 2014, 41(5): 051909 (1-9).
    [20] ZHAO W, NIU T, XING L, et al. Using edge-preserving algorithm with non-local mean for significantly improved image-domain material decomposition in dual-energy CT[J]. Physics in Medicine & Biology, 2016, 61(3): 1332−1351.
    [21] 陈佩君, 冯鹏, 伍伟文, 等. 基于图像总变分和张量字典的多能谱CT材料识别研究[J]. 光学学报, 2018, 38(11): 1111002(1-8).

    CHEN P J, FENG P, WU W W, et al. Material discrimination by multi-spectral CT based on lmage total variation and tensor dictionary[J]. Acta Optica Sinica 2018, 38(11): 1111002(1-8). (in Chinese).
    [22] DENG G, CHEN M, HE P, et al. The experimental study on geometric calibration and material discrimination for in Vivo dual-energy CT imaging[J]. Biomedical Research International, 2019, 2019: 7614589.
    [23] NIU T, DONG X, PETRONGOLO M, et al. Iterative image-domain decomposition for dual-energy CT[J]. Medical Physics, 2014, 41(4): 041901(1−10).
    [24] TANG S, YANG M, HU X, et al. Multiscale penalized weighted least-squares image-domain decomposition for dual-energy CT[C]//2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2015: 1-6.
    [25] LI Z, RAVISHANKAR S, LONG Y, et al. DECT-MULTRA: Dual-energy CT image decomposition with learned mixed material models and efficient clustering[J]. IEEE Transactions on Medical Imaging, 2020, 39(4): 1223−1234. doi: 10.1109/TMI.2019.2946177
    [26] 王冲旭, 陈平, 潘晋孝, 等. 基于迭代残差网络的双能CT图像材料分解研究[J]. CT理论与应用研究, 2022,31(1): 47−54. DOI: 10.15953/j.1004-4140.2022.31.01.05.

    WANG C X, CHEN P, PAN J X, et al. Research on material decomposition of dual-energy CT image based on iterative residual network[J]. CT Theory and Applications, 2022, 31(1): 47−54. DOI: 10.15953/j.1004-4140.2022.31.01.05. (in Chinese).
    [27] XU Y, YAN B, ZHANG J, et al. Image decomposition algorithm for dual-energy computed tomography via fully convolutional network[J]. Computational and Mathematical Methods in Medicine, 2018, 2018(1): 2527516.
    [28] 朱冬亮, 文奕, 陶欣. 深度学习在生物医学领域的应用进展述评[J]. 世界科技研究与发展, 2020,42(5): 510−519.

    ZHU D L, WEN Y, TAO X. A Review of the application progress of deep learning in biomedical field[J]. World Sci-Tech R & D, 2020, 42(5): 510−519. (in Chinese).
    [29] KAWAHARA D, SAITO A, OZAWA S, et al. Image synthesis with deep convolutional generative adversarial networks for material decomposition in dual-energy CT from a kilovoltage CT[J]. Computers in Biology and Medicine, 2021, 128: 104111. doi: 10.1016/j.compbiomed.2020.104111
    [30] LYU T, ZHAO W, ZHU Y, et al. Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network[J]. Medical Image Analysis, 2021, 70: 102001. doi: 10.1016/j.media.2021.102001
    [31] CLARK D P, HOLBROOK M, BADEA C T. Multi-energy CT decomposition using convolutional neural networks[C]//Medical Imaging 2018: Physics of Medical Imaging. International Society for Optics and Photonics, 2018, 10573: 105731O.
    [32] ZHANG W, ZHANG H, WANG L, et al. Image domain dual material decomposition for dual-energy CT using butterfly network[J]. Medical Physics, 2019, 46(5): 2037−2051. doi: 10.1002/mp.13489
    [33] SHI Z, LI H, CAO Q, et al. A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks[J]. Medical Physics, 2021, 48(6): 2891−2905. doi: 10.1002/mp.14828
    [34] 郭志鹏. Micro-CT重建图像质量增强的方法研究[D]. 西安: 西安电子科技大学, 2017.

    GUO Z P. Research on image quality enhancement method in micro-CT reconstruction[D]. Xi'an: Xidian University, 2017. (in Chinese).
    [35] LIU X, YU L, PRIMAK A N, et al. Quantitative imaging of element composition and mass fraction using dual-energy CT: Three-material decomposition[J]. Medical Physics, 2009, 36(5): 1602−1609. doi: 10.1118/1.3097632
    [36] MENDONÇA P R, LAMB P, SAHANI D V. A flexible method for multi-material decomposition of dual-energy CT images[J]. IEEE Transactions on Medical Imaging, 2014, 33(1): 99−116. doi: 10.1109/TMI.2013.2281719
    [37] JIANG Y, XUE Y, LYU Q, et al. Noise suppression in image-domain multi-material decomposition for dual-energy CT[J]. IEEE Transactions on Biomedical Engineering, 2020, 67(2): 523−535. doi: 10.1109/TBME.2019.2916907
    [38] LEE H, KIM H J, LEE D, et al. Improvement with the multi-material decomposition framework in dual-energy computed tomography: A phantom study[J]. Journal of the Korean Physical Society, 2020, 77(6): 515−523. doi: 10.3938/jkps.77.515
    [39] 降俊汝, 余海军, 龚长城, 等. 基于双能CT图像域的DL-RTV多材料分解研究[J]. 光学学报, 2020,40(21): 2111004(1−12).

    JIANG J R, YU H J, GONG C C, et al. Image-domain multimaterial decomposition for dual-energy CT based on dictionary learning and relative total variation[J]. Acta Optica Sinica, 2020, 40(21): 2111004(1−12). (in Chinese).
    [40] LONG Y, FESSLER J A. Multi-material decomposition using statistical image reconstruction for spectral CT[J]. IEEE Transactions on Medical Imaging, 2014, 33(8): 1614−26. doi: 10.1109/TMI.2014.2320284
    [41] XUE Y, RUAN R, HU X, et al. Statistical image-domain multimaterial decomposition for dual-energy CT[J]. Medical Physics, 2017, 44(3): 886−901. doi: 10.1002/mp.12096
    [42] DING Q, NIU T, ZHANG X, et al. Image-domain multimaterial decomposition for dual-energy CT based on prior information of material images[J]. Medical Physics, 2018, 45(8): 3614−3626. doi: 10.1002/mp.13001
    [43] PATINO M, PROCHOWSKI A, AGRAWAL M D, et al. Material separation using dual-energy CT: Current and emerging applications[J]. Radiographics, 2016, 36(4): 1087−1105. doi: 10.1148/rg.2016150220
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  • 收稿日期:  2021-12-17
  • 录用日期:  2022-04-06
  • 网络出版日期:  2022-04-18

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