基于分块的肺4DCT图像超分辨率重建

doi:10.3969/j.issn.0258-8021.2015.02.005

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摘要肺4DCT在肺癌的治疗中，对于准确定位肿瘤靶区、实现个体化精确放疗具有重要的应用价值。由于放射剂量的限制，使得肺4DCT图像分辨率较低。提出一种基于图像分块的超分辨率重建技术，提升肺4DCT图像的分辨率。首先，将图像分解为一系列重叠图像块；其次，利用Active Demons配准算法，自适应选取若干幅与目标图像块对应结构相似的其他相位图像块，并估计出它们之间的运动变形场；而后，采用迭代反投影（IBP）算法，重建得到高分辨率的图像块；将得到的所有高分辨率图像块拼接后，最终输出完整的高分辨率肺4DCT图像。利用由德克萨斯安德森肿瘤中心DIR实验室采集并开放的公共数据集评价所提出的算法，该数据集由10组肺4DCT数据组成，每组数据包含10个相位。从每组数据中，选取不同相位图像进行实验。量化评价结果表明，所提出的方法相比反投影（BP）算法、全局迭代背投影算法，图像平均梯度显著提高（反投影算法765±044，全局迭代背投影算法792±043，本算法892±050，P < 0001）。视觉评价结果同时显示，所提出的方法能够有效消除伪影，得到结构显著增强、清晰度更高的肺4DCT图像。

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	王婷婷吴秀秀张煜^*

关键词 ：肺4DCT, 图像分块, 自适应选择, 超分辨率重建

Abstract：Lung 4DCT is of great values in the application of tumor localization and individualized precise radiotherapy. However, due to the dose limitation, 4DCT data are in low resolution. In this paper, we proposed a patchbased superresolution reconstruction approach to enhance the resolution of 4DCT images. First, we divided the images into a number of overlapping patches. Then, Active Demons registration algorithm was utilized to adaptively select corresponding patches in each phase for the specific patch, and obtain the motion fields between the patches simultaneously. After that, iterative back projection (IBP) algorithm was adopted to reconstruct highresolution patch. At last, we assembled all patches into a final output high resolution image. A public dataset provided by the DIRlab at the University of Texas M.D. Anderson Cancer Center (Houston, TX) was employed to evaluate the proposed method. The dataset consists of 10 groups of lung 4DCT data, each group contains 10 phases. Images of different phases are selected to perform the above procedure for experiment. Quantitative evaluation results indicate that the proposed method significantly increases image average gradient value (892±050, P<0001) compared with back projection (BP) algorithm (765±044) and global IBP algorithm (792±043). Qualitative evaluation results also show that our approach can eliminate artifacts effectively and get clearer lung 4DCT images with significantly enhanced structures.

Key words： lung 4DCT data patch adaptive selection superresolution reconstruction

基金资助:国家自然科学基金（31271067）；广东省自然科学基金（S2013010014049）

引用本文:

王婷婷吴秀秀张煜^*. 基于分块的肺4DCT图像超分辨率重建[J]. 中国生物医学工程学报, 2015, 34(2): 160-167.
Wang Tingting Wu Xiuxiu Zhang Yu^*. PatchBased SuperResolution Reconstruction for Lung 4DCT Images. journal1, 2015, 34(2): 160-167.

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http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2015.02.005 或 http://cjbme.csbme.org/CN/Y2015/V34/I2/160

［1］董志伟, 乔友林, 李连弟, 等. 中国癌症控制策略研究报告［J］. 中国肿瘤, 2002, 11(5): 250-260.
［2］张金忠, 戈伟, 张园如, 等. CT/CT 图像融合用于非小细胞肺癌放射治疗对 V_ (20) 及放射性肺炎影响的临床研究［J］. 临床肿瘤学杂志, 2011, 16(2): 154-157.
［3］Keall P. 4dimensional computed tomography imaging and treatment planning ［J］. Seminars in Radiation Oncology， 2004, 14(1): 81-90. 
［4］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.
［5］Khan F, Bell G, Antony J, et al. The use of 4DCT to reduce lung dose: A dosimetric analysis ［J］. Medical Dosimetry, 2010, 34(4): 273-278.
［6］Li Tianfang, Schreibmann E, Thorndyke B, et al. Radiation dose reduction in fourdimensional computed tomography ［J］. Medical Physics, 2005, 32(12): 3650-3660.
［7］肖珊, 王婷婷, 张煜. 基于运动估计的肺 4DCT 图像冠矢状面超分辨率重建［J］. 中国生物医学工程学报, 2014, 33(2): 170-177.
［8］Wu XiuXiu, Xiao Shan, Zhang Yu. Registration based superresolution reconstruction for lung 4DCT［C］//36th Annual International Conference of the IEEE EMBS. Chicago: IEEE, 2014: 2444-2447.
［9］林相波，邱天爽. Active Demons可变形图像配准算法研究［J］.中国生物医学工程学报，2008,27(4):636-641.
［10］Irani M, Peleg S. Improving resolution by image registration［J］. CVGIP: Graphical Models and Image Processing, 1991, 53(3): 231-239.
［11］Wang He, Dong Lei. Validation of an accelerated“demons”algorithm for deformable image registration in radiation therapy［J］．Physics in Medicine Biology,2005,50(12):2887-2905．
［12］Thirion JP. Image matching as a diffusion process: an analogy with Maxwell's demons［J］. Medical Image Analysis, 1998, 2(3): 243-260.
［13］Buades A, Coll B, Morel JM. A nonlocal algorithm for image denoising［C］// Conference on Computer Vision and Pattern Recognition (CVPR). San Diego: IEEE, 2005, 2: 60-65. 
［14］Park SC, Park MK, Kang MG. Superresolution image reconstruction: a technical overview ［J］. IEEE Signal Processing Magazine, 2003, 20(3): 21-36.
［15］Elad M, Feuer A. Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images ［J］. IEEE Transactions on Image Processing, 1997, 6(12): 1646-1658.
［16］Irani M, Peleg S. Motion analysis for image enhancement: Resolution, occlusion, and transparency ［J］. Journal of Visual Communication and Image Representation, 1993, 4(4): 324-335.
［17］徐美芳, 刘晶红. 基于边缘保持的航拍图像凸集投影超分辨率重建算法［J］. 液晶与显示, 2010, 25(6): 873-878.
［18］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 in Medicine and Biology, 2009, 54(7): 1849-1870.
［19］柯丽，林筱，杜强，等. 基于反投影的 MIT 动态图像重建方法研究［J］.仪器仪表学报，2013,34(2):394-341.
［20］Brown MB, Forsythe AB. Robust tests for the equality of variances ［J］. Journal of the American Statistical Association, 1974, 69(346): 364-367.
［21］Bland JM, Altman DG. Multiple significance tests: the Bonferroni method ［J］. Br Med J, 1995,310(6986):1073-1075.
［22］Popa T, Ibáez L, Cleary K, et al. ITK implementation of deformable registration methods for timevarying (4D) imaging data［C］//Medical Imaging 2006: Visualization, ImageGuided Procedures and Display. International Society for Optics and Engineering. San Diego: SPIE, 2006,6141:750-759. 
［23］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.