基于弹性配准的肺4D-CT中间相位图像重建

doi:10.11772/j.issn.1001-9081.2015.04.1120

计算机应用 ›› 2015, Vol. 35 ›› Issue (4): 1120-1123.DOI: 10.11772/j.issn.1001-9081.2015.04.1120

基于弹性配准的肺4D-CT中间相位图像重建

耿丹丹, 王婷婷, 曹蕾, 张煜

南方医科大学生物医学工程学院, 广州 510000

收稿日期:2014-10-20 修回日期:2015-01-07 出版日期:2015-04-10 发布日期:2015-04-08
通讯作者: 张煜
作者简介:耿丹丹(1991-),女,河南卫辉人,硕士研究生,主要研究方向:医学图像处理; 王婷婷(1990-),女,甘肃兰州人,硕士研究生,主要研究方向:医学图像处理; 曹蕾(1974-),女,湖南沅江人,副教授,主要研究方向:医学图像处理、软件工程; 张煜(1975-),男,江西南昌人,教授,主要研究方向:医学图像处理。
基金资助:
国家自然科学基金资助项目(31271067, 61102114);广东省自然科学基金资助项目(S2013010014049)。

Reconstruction of images at intermediate phases of lung 4D-CT data based on deformable registration

GENG Dandan, WANG Tingting, CAO Lei, ZHANG Yu

School of Biomedical Engineering, Southern Medical University, Guangzhou Guangdong 510000, China

Received:2014-10-20 Revised:2015-01-07 Online:2015-04-10 Published:2015-04-08

摘要/Abstract

摘要：

针对肺四维计算机断层摄影(4D-CT)数据获取时辐射剂量高的缺点,提出了一种基于弹性配准的肺4D-CT中间相位图像重建方法,利用已知的两个特殊呼吸相位图像,重建出其他中间呼吸相位图像。其具体过程是:选择呼吸周期内的两个相位图像分别作为参考图像和浮动图像,首先用Active Demons配准方法估计出两个相位图像间的运动位移场;然后根据呼吸运动过程的线性假设,相应地求出浮动图像到中间相位图像的运动位移,继而重建出中间相位图像。实验结果表明,重建出的中间相位图像不仅在视觉上与真实图像非常接近,而且用标记点方法量化评价的平均误差也在3 mm内。所提基于弹性配准的重建方法能较准确地重建出肺4D-CT中间相位图像。

关键词: 肺四维计算机断层摄影, 图像重建, 呼吸运动, Active Demons配准方法

Abstract:

Due to the high radiation dose to the patient when acquiring lung four Dimensional Computed Tomography (4D-CT) data, this paper proposed a method for deriving the phase-binned 4D-CT image sets through deformable registration of the images acquired at some known phases. First, Active Demons registration algorithm was employed to estimate the motion field between inhale and exhale phases. Then, images at an intermediate phase were reconstructed by a linear interpolation of the deformation coefficients. The experiment results showed that the images at intermediate phases could be reconstructed efficiently. The quantitative analysis of landmark point displacements showed that 3 mm accuracy was achievable. The different maps of reconstructed and acquired images illustrated the similar level of success. The proposed method can accurately reconstruct images at intermediate phases of lung 4D-CT data.

Key words: lung four Dimensional Computed Tomography (4D-CT), image reconstruction, respiratory motion, Active Demons registration algorithm

中图分类号:

TP751

耿丹丹, 王婷婷, 曹蕾, 张煜. 基于弹性配准的肺4D-CT中间相位图像重建[J]. 计算机应用, 2015, 35(4): 1120-1123.

GENG Dandan, WANG Tingting, CAO Lei, ZHANG Yu. Reconstruction of images at intermediate phases of lung 4D-CT data based on deformable registration[J]. Journal of Computer Applications, 2015, 35(4): 1120-1123.

参考文献

[1] KEALL P. 4-dimensional computed tomography imaging and treatment planning[J]. Seminars in Radiation Oncology, 2004, 14(1): 81-90.
[2] SU P, XUE Z, YANG J. Estimation of lung respiratory motion based on 4D CT [J]. Journal of Data Acquisition and Processing, 2013, 28(5): 608-613.(苏坡, 薛忠, 杨建华. 4D CT 图像的肺部呼吸运动估计[J]. 数据采集与处理, 2013, 28(5): 608-613.)
[3] PARTRIDGE M, TREE A, BROCK J, et al. Improvement in tumour control probability with active breathing control and dose escalation: a modelling study[J]. Radiotherapy and Oncology, 2009,91(3):325-329.
[4] WUNDERINK W, MENDEZ-ROMERO A, de KRUIJF W, et al. Reduction of respiratory liver tumor motion by abdominal compression in stereotactic body frame, analyzed by tracking fiducial markers implanted in liver[J]. International Journal of Radiation Oncology Biology Physics, 2008, 71(3): 907-915.
[5] GIRAUD P, MORVAN E, CLAUDE L, et al. Respiratory gating techniques for optimization of lung cancer radiotherapy [J]. Journal of Thoracic Oncology, 2011,6(12):2058-68.
[6] LI T, SCHREIBMANN E, THORNDYKE B, et al. Radiation dose reduction in four-dimensional computed tomography[J]. Medical Physics, 2005,32(12): 3650-3660.
[7] SCHREIBMANN E, CHEN G T Y, XING L. Image interpolation in 4D CT using a B-spline deformable registration model[J]. International Journal of Radiation Oncology Biology Physics, 2006, 64(5): 1537-1550.
[8] TIAN Z, JIA X, DONG B, et al. Low-dose 4DCT reconstruction via temporal nonlocal means[J]. Medical Physics, 2011, 38(3): 1359-1365.
[9] KIM K S, YE J C. Low-dose limited view 4DCT reconstruction using patch-based low rank regularization[C]// Proceedings of the 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference. Piscataway: IEEE Press, 2013:1-4.
[10] THIRION J P. Image matching as diffusion process: an analogy with Maxwell's demons[J]. Medical Image Analysis,1998,2(3): 243-260.
[11] WANG H, LEI D, O'DANIEL J. Validation of an accelerated 'demons' algorithm for deformable image registration in radiation therapy [J].Physics in Medicine Biology, 2005,50(12):2887-2905.
[12] JIA Y, ZHU X, LIN E, et al. Image registration by combination of gradient and curvature driven — large deformation non-rigid image registration[J]. Computer Engineering and Applications, 2013,49(11):191-195.(贾义亭,祝轩,蔺恩标,等.梯度与曲率变形力相结合的图像配准方法——大形变非刚性图像配准[J].计算机工程与应用,2013,49(11):191-195.)
[13] XU S, SONG E, XU X. Non-rigid mammogram registration based on improved Demons algorithm[J]. Journal of Image and Graphics, 2009,14(12);2566-2571.(徐胜舟,宋恩民,许向阳.基于改进Demons算法的乳腺X线摄片非刚性配准[J].中国图象图形学报,2009,14(12);2566-2571.)
[14] SARRUT D, BOLDEA V, MIGUET S, et al. Simulation of four-dimensional CT images from deformable registration between inhale and exhale breath-hold CT scans[J]. Medical Physics, 2006, 33(3): 605-617.
[15] CASTILLO R, CASTILLO E, GUERRA R, et al. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets[J]. Physics Medical Biology, 2009, 54(7): 1849-1870.
[16] YANG X, PEI J. Lung deformation estimation using spatially mean shift for 4D-CT [C]// Proceedings of the 2013 IEEE International Conference on Bioinformatics and Biomedicine. Piscataway: IEEE Press, 2013:237-242.
[17] BOLDEA V, SHARP G C, JIANG S B, et al. 4D-CT lung motion estimation with deformable registration: quantification of motion nonlinearity and hysteresis[J]. Medical Physics, 2008, 35(3): 1008-1018.

基于弹性配准的肺4D-CT中间相位图像重建

Reconstruction of images at intermediate phases of lung 4D-CT data based on deformable registration

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	席雅睿, 乔志伟, 温静, 张艳娇, 杨雯晶, 闫慧文. 基于Chambolle-Pock算法框架的高阶TV图像重建算法[J]. 计算机应用, 2020, 40(6): 1793-1798.
[2]	王治忠, 庞晨. 基于鸽视顶盖神经元动作电位的图像重建[J]. 计算机应用, 2020, 40(3): 832-836.
[3]	叶杨, 蔡琼, 杜晓标. 基于非局部通道注意力机制的单图像超分辨率方法[J]. 计算机应用, 2020, 40(12): 3618-3623.
[4]	杜年茂, 徐佳陈, 肖志勇. 基于卷积神经网络的欠采样脑部核磁共振图像重建方法[J]. 计算机应用, 2020, 40(10): 3060-3065.
[5]	王文杰, 乔志伟, 牛蕾, 席雅睿. 自适应步长非局部全变分约束迭代图像重建算法[J]. 计算机应用, 2020, 40(1): 245-251.
[6]	杜凯敏, 康宝生. 基于图像块分类的图像超分辨率重建[J]. 计算机应用, 2019, 39(2): 577-581.
[7]	刘晓晖, 路利军, 冯前进, 陈武凡. 基于Moreau-包络的近似平滑迭代磁共振图像重建算法[J]. 计算机应用, 2018, 38(7): 2076-2082.
[8]	陈晨, 赵建伟, 曹飞龙. 基于四通道卷积稀疏编码的图像超分辨率重建方法[J]. 计算机应用, 2018, 38(6): 1777-1783.
[9]	杨伟, 谢维成, 蒋文波, 石林玉. 基于自相似性车载采集城市街景图像的重建[J]. 计算机应用, 2017, 37(3): 817-822.
[10]	周尚波, 王李平, 尹学辉. 分数阶偏微分方程在图像处理中的应用[J]. 计算机应用, 2017, 37(2): 546-552.
[11]	郭晓, 谭文安. 基于级联深度卷积神经网络的高性能图像超分辨率重构[J]. 计算机应用, 2017, 37(11): 3124-3127.
[12]	吉晓红, 熊淑华, 何小海, 陈洪刚. 多阶导数自适应视频超分辨率重建[J]. 计算机应用, 2016, 36(4): 1092-1095.
[13]	姜自波, 赵景秀, 张元科, 孟静. 快速在体光声计算层析图像重建方法[J]. 计算机应用, 2016, 36(3): 811-814.
[14]	张晓梦杨宏成张涛. 定步长压缩感知锥束CT重建算法[J]. 计算机应用, 2014, 34(2): 553-557.
[15]	上官宏刘祎张权桂志国. 基于解剖非局部先验的模糊扩散PET重建算法[J]. 计算机应用, 2013, 33(09): 2627-2630.