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基于边缘保护尺度空间的形变配准方法及在自适应放疗中的应用

李登旺 李洪升 王惠 王洪君 尹勇 彭玉华

李登旺, 李洪升, 王惠, 王洪君, 尹勇, 彭玉华. 基于边缘保护尺度空间的形变配准方法及在自适应放疗中的应用. 自动化学报, 2012, 38(5): 751-758. doi: 10.3724/SP.J.1004.2012.00751
引用本文: 李登旺, 李洪升, 王惠, 王洪君, 尹勇, 彭玉华. 基于边缘保护尺度空间的形变配准方法及在自适应放疗中的应用. 自动化学报, 2012, 38(5): 751-758. doi: 10.3724/SP.J.1004.2012.00751
LI Deng-Wang, LI Hong-Sheng, WANG Hui, WANG Hong-Jun, YIN Yong, PENG Yu-Hua. Deformable Registration Method Using Edge Preserving Scale Space withApplication in Adaptive Radiation Therapy. ACTA AUTOMATICA SINICA, 2012, 38(5): 751-758. doi: 10.3724/SP.J.1004.2012.00751
Citation: LI Deng-Wang, LI Hong-Sheng, WANG Hui, WANG Hong-Jun, YIN Yong, PENG Yu-Hua. Deformable Registration Method Using Edge Preserving Scale Space withApplication in Adaptive Radiation Therapy. ACTA AUTOMATICA SINICA, 2012, 38(5): 751-758. doi: 10.3724/SP.J.1004.2012.00751

基于边缘保护尺度空间的形变配准方法及在自适应放疗中的应用

doi: 10.3724/SP.J.1004.2012.00751
详细信息
    通讯作者:

    王洪君, 博士, 山东大学信息科学与工程学院教授. 主要研究方向为医学图像处理与分析, 图像引导放射治疗.

Deformable Registration Method Using Edge Preserving Scale Space withApplication in Adaptive Radiation Therapy

  • 摘要: 计划CT图像与锥形束CT (Cone beam CT, CBCT)图像的配准是基于CBCT图像引导放射治疗(Image guided radiation therapy, IGRT)系统中实现自适应放疗(Adaptive radiation therapy, ART)的关键部分.边缘保护多尺度空间基于非线性扩散模型,可以为基于互信息的配准提供丰富的空间位置信息.为了提高系统中配准算法性能,本文提出了一种基于边缘保护尺度空间与自由形变模型(Free form deformation, FFD)相结合的多尺度形变配准方法.我们采用了在不同的尺度上根据精细程度选择相应的自由形变控制点数,由粗及精地恢复形变.同时, 提出了自动获取非线性扩散模型中平滑参数λ的方法来实现全自动配准. 实验结果表明,本文提出的方法用于基于CBCT的图像引导放射系统时,可实现日常放疗时的CBCT图像和计划CT图像准确且快速的配准.通过获得的形变域,可实现CBCT图像肿瘤靶区、危及器官(Organ at risk, OR)和等剂量线的自动勾画,从而实现剂量体积直方图(Dose volume histograms, DVH)分析.最终实现了放疗计划从CT到CBCT的自适应转移.
  • [1] Westberg J, Jensen HR, Bertelsen A, Brink C. Reduction of cone-beam CT scan time without compromising the accuracy of the image registration in IGRT. Acta Oncologica, 2010, 49(2): 225-229[2] Gottlieb K L, Hansen C R, Hansen O, Westberg J, Brink C. Investigation of respiration induced intra- and inter-fractional tumour motion using a standard cone beam CT. Acta Oncologica, 2010, 49(7): 1192-1198[3] Cheng B S, Yang Y, Li F, Yue N J, Ding C X, Komanduri K, Huq S, Heron D E. Performance characteristics and auality assurance aspects of kilovoltage cone-beam CT on medical linear accelerator. Medical Dosimetry, 2007, 32(2): 80-85[4] Paquin D, Levy D, Xing L. Multiscale registration of planning CT and daily cone beam CT images for adaptive radiation therapy. Medical Physics, 2009, 36(1): 4-11[5] Brock K K, Dawson L A, Sharpe M B, Moseley D J, Jaffray D A. Feasibility of a novel deformable image registration technique to facilitate classification, targeting, and monitoring of tumor and normal tissue. International Journal of Radiation Oncology, Biology, Physics, 2006, 64(1): 1245-1254[6] Nithiananthan S, Brock K K, Daly M J, Chan H, Irish J C, Siewerdsen J H. Demons deformable registration for CBCT-guided procedures in the head and neck: convergence and accuracy. Medical Physics, 2009, 36(10): 4755-4764[7] Ostergaard N K, De Senneville B D, Elstrom U V, Tanderup K, Sorensen T S. Acceleration and validation of optical flow based deformable registration for image-guided radiotherapy. Acta Oncologica, 2008, 47(7): 1286-1293[8] Bajcsy R, Kovačič S. Multiresolution elastic matching. Computer Vision, Graphics, and Image Processing, 1989, 46(1): 1-21[9] Loeckx D, Slagmolen P, Maes F, Vandermeulen D, Suetens P. Nonrigid image registration using conditional mutual information. IEEE Transactions on Medical Imaging, 2010, 29(1): 19-29[10] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 1992, 60(1-4): 259-268[11] Chan T F, Esedoglu S. Aspects of total variation regularized L-1-function approximation. SIAM Journal on Applied Mathematics, 2005, 65(1): 1817-1837[12] Chen T, Huang T S, Yin W T, Zhou X S. A new coarse-to-fine framework for 3d brain MR image registration. In: Proceedings of the 1st International Conference on Computer Vision for Biomedical Image Applications. Berlin, Heidelberg: Springer-Verlag, 2005. 114-124[13] Rueckert D, Sonoda L I, Hayes C, Hill D L G, Leach M O, Hawkes D J. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging, 1999, 18(8): 712-721[14] Holden M. A review of geometric transformations for nonrigid body registration. IEEE Transactions on Medical Imaging, 2008, 27(1): 111-128[15] Goldfarb D, Yin W T. Parametric maximum flow algorithms for fast total variation minimization. SIAM Journal on Scientific Computing, 2009, 31(1): 3712-3743
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出版历程
  • 收稿日期:  2011-08-31
  • 修回日期:  2011-12-02
  • 刊出日期:  2012-05-20

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