Iterative image reconstruction for differential phase contrast CT based on compressive sensing

doi:10.3724/SP.J.1087.2013.01732

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (06): 1732-1736.DOI: 10.3724/SP.J.1087.2013.01732

• Network and distributed techno • Previous Articles Next Articles

Iterative image reconstruction for differential phase contrast CT based on compressive sensing

QIN Feng¹,SUN Fengrong¹,SONG Shangling²,ZHANG Xinping¹,LI Xincai¹

1. School of Information Science and Engineering, Shandong University, Jinan Shandong 250100, China
2. Equipment Department of the Second Hospital, Shandong University, Jinan Shandong 250033, China

Received:2012-12-17 Revised:2013-02-28 Online:2013-06-05 Published:2013-06-01
Contact: QIN Feng

基于压缩感知的微分相衬CT迭代图像重建

秦峰¹,孙丰荣¹,宋尚玲²,张新萍¹,李新彩¹

1. 山东大学信息科学与工程学院，济南 250100
2. 山东大学第二医院设备部，济南 250033

通讯作者: 秦峰
作者简介:秦峰（1989-），男，安徽六安人，硕士研究生，主要研究方向：医学图像处理；孙丰荣（1969-），山东泰安人，教授，博士，主要研究方向：医学图像处理；宋尚玲（1978-），山东淄博人，博士研究生，主要研究方向：计算机视觉、生物特征识别；张新萍（1983-），女，山东德州人，硕士研究生，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(60872092);山东省自然科学基金资助项目（ZR2010FM012）;教育部留学回国人员科研启动基金资助项目

Abstract

Abstract: The X-ray phase contrast Computed Tomography (CT) can produce high contrast images by the X-ray phase information alteration, which comes forth after the X-ray passes through the sample, and it is highly favorable to the imaging of light elements and can get much higher contrast resolution than the absorption contrast CT. Grating-based Differential Phase Contrast CT (DPC-CT) shows great clinical prospects due to the possibility of using a conventional X-ray source, but the X-ray radiation dose issue limits its clinical applications. Concerning such inadequacies, an image reconstruction method for DPC-CT named DD-L1 was proposed. This algorithm combined Compressive Sensing (CS) theory with CT iterative reconstruction technique and introduced distance driven forward and backward projection computation strategy. The experimental results show that DD-L1 algorithm can generate tomographic images of higher quality even when the projection data is incomplete.

Key words: differential contrast Computed Tomography (CT), image reconstruction, Compressive Sensing (CS), distance driven

摘要： X-射线相衬计算机断层成像(CT)通过X-射线穿过样品后相位信息的改变来得到高衬度的图像，特别适用于轻元素的成像，并且可以获得远高于传统吸收衬度CT的密度分辨率。基于光栅的微分相衬CT（DPC-CT）由于可以使用常规的X射线光源而有着巨大的临床应用前景，但DPC-CT成像的X-射线辐射剂量问题尤为突出，是其走向实际应用的瓶颈。针对上述不足，提出了一种微分相衬CT迭代图像重建算法（DD-L1），该方法将压缩感知（CS）理论和CT迭代图像重建技术相结合并引入距离驱动（DD）的正/反投影运算计算策略。仿真实验结果表明，DD-L1算法能够在投影数据不完备的情况下得到较高质量的重建图像。

关键词: 微分相衬CT, 图像重建, 压缩感知, 距离驱动

CLC Number:

TP391.41

QIN Feng SUN Fengrong SONG Shangling ZHANG Xinping LI Xincai. Iterative image reconstruction for differential phase contrast CT based on compressive sensing[J]. Journal of Computer Applications, 2013, 33(06): 1732-1736.

秦峰孙丰荣宋尚玲张新萍李新彩. 基于压缩感知的微分相衬CT迭代图像重建[J]. 计算机应用, 2013, 33(06): 1732-1736.

References

[1] KOTTLER C, REVOL V, KAUFMANN R, et al. X-ray phase contrast imaging of soft tissue specimens［C］// Nuclear Science Symposium and Medical Imaging Conference. Piscataway:IEEE, 2011: 4437-4440.

[2] CHEN R C, XIE H L, DU G H, et al. Experimental investigation on X-ray micro-computed tomography by phase retrieval with born approximations［J］. Acta Optica Sinica, 2010,30(4):1106-1111.

[3] CHAPMAN D, THOMLINSON W, JOHNSTON R E, et al. Diffraction enhanced X-ray imaging ［J］. Physics in Medicine and Biology,1997,42(11):2015-2025.

[4] WEITKAMP T, DIAZ T, DAVID C, et al. X-ray phase imaging with a grating interferometer［J］. Optics Express,2005,13(16):6296-6304.

[5] 陈博,朱佩平,刘宜晋,等. X射线光栅相位成像的理论和方法［J］. 物理学报,2008,57(3):1581.

[6] ZHU P P, ZHANG K, WANG Z L, et al. Low-dose, simple, and fast grating-based X-ray phase-contrast imaging［J］. Proceedings of the National Academy of Sciences of the USA,2010,107(31):13576-13581.

[7] 李镜,刘文杰,朱佩平,等. 基于光栅相衬成像的扇束螺旋CT重建算法［J］. 光学学报,2010,30(2):421-427.

[8] WANG Z T, ZHANG L, HUANG Z F, et al. An ART iterative reconstruction algorithm for computed tomography of diffraction enhanced imaging［J］. Chinese Physics C,2009,33(11):975-980.

[9] 张凯,朱佩平,黄万霞,等. 代数迭代重建算法在折射衬度CT中的应用［J］. 物理学报,2008,57(6):3410-3418.

[10] DAVID C, NOHAMMER B, SOLAK H, et al. Differential X-ray phase contrast imaging using a shearing interferometer［J］. Applied Physics Letters,2002,81(17):3287-3289.

[11] PFEIFFER F, WEITKAMP T, BUNK O, et al. Phase retrieval and differential phase-contrast imaging with low-brilliance X-ray sources［J］. Nature Physics,2006,2(4):258-261.

[12] MOMOSE A. Demonstration of phase-contrast X-ray computed tomography using an X-ray interferometer［J］. Nuclear Instruments and Methods in Physics Research,1995,352(3):622-628.

[13] PAVLOV K M, KEWISH C M, DAVIS J R, et al. A variant on the geometrical optics approximation in diffraction enhanced tomography［J］. Journal of Physics D:Applied Physics,2001,34(10A):168-172.

[14] MAKSIMENKO A, ANDO M, HIROSHI S, et al. Computed tomographic reconstruction based on X-ray refraction contrast［J］. Applied Physics Letters,2005,86(12): 86-88.

[15]HUANG Z F, KANG K J, LI Z, et al. Direct computed tomographic reconstruction for directional-derivative projections of computed tomography of diffraction enhanced imaging［J］. Applied Physics Letters,2006,89(4):041124.

[16]CHEN G H, TANG J, LENG S. Prior Image Constrained Compressed Sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly under-sampled projection data sets［J］.Medical Physics,2008,35(2):660-663.

[17]SIDKY E Y, KAO C M, PAN X C. Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT［J］. Journal of X-Ray Science and Technology,2006,14(2):119-139.

[18]DAVIS T J, GAO D, GUREYEV T E, et al. Phase-contrast imaging of weakly absorbing materials using hard X-rays[J]. Nature,1995,373(6515):595-597.

[19]王军华,黄知涛,周一宇,等. 压缩感知理论中的广义不相关性准则［J］.信号处理,2012,5(5):675-679.

[20]李镜,孙怡. 基于L1范数的微分相位衬度CT稀疏角度重建算法［J］. 光学学报, 2012, 32(3):0311002.

[21]DE-MAN B, BASU S. Distance-driven projection and back-projection in three dimensions［J］. Physics in Medicine and Biology, 2004,49(11):2463-2475.

[22]李涛涛,李华,刁麓弘.复杂物体的X射线相衬成像三维仿真［J］. 系统仿真学报, 2009, 21(1):32-35.

Iterative image reconstruction for differential phase contrast CT based on compressive sensing

基于压缩感知的微分相衬CT迭代图像重建

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