基于改进谱投影梯度算法的X射线发光断层成像

侯榆青; 贾涛; 易黄建; 张海波; 贺小伟

doi:10.3788/OPE.20172501.0042

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基于改进谱投影梯度算法的X射线发光断层成像

现代应用光学 | 更新时间：2020-07-07

- 基于改进谱投影梯度算法的X射线发光断层成像
- X-ray luminescence computed tomography based on improved spectral projected gradient algorithm
- 光学精密工程 2017年25卷第1期页码：42-49
- 作者机构：
  
  西北大学信息科学与技术学院, 陕西西安 710127
- 作者简介：
  
  [ "侯榆青(1963-)，女，陕西榆林人，教授，1984年于西北大学获得学士学位，1990年于中国科学院西安光机所获得硕士学位，主要从事光学分子影像、医学图像处理及三维重建等方面的研究。E-mail:houyuqin@nwu.edu.cn" ]
  [ "贾涛(1990-),男,陕西黄陵人,硕士研究生,2014年于西安文理学院获得学士学位,主要从事医学图像处理方向的研究。E-mail:jiatao@stumail.nwu.edu.cn" ]
  易黄建(1985-),女,湖南株洲人,博士,讲师,2008年、2013年于西安电子科技大学分别获得学士、博士学位,主要从事荧光分子断层成像、优化算法的研究。E-mail:yhj2014@nwu.edu.cn E-mail:yhj2014@nwu.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(No.61372046，No.11571012，No.61640418);中国博士后科学基金资助项目;陕西省科技计划资助项目(No.2015KW-002);陕西省自然科学研究计划资助项目(No.2015JM6322);陕西省教育厅基金资助项目(No.15JK1726);陕西省教育厅专项科研计划资助项目(No.14JK1578，No.16JK1772);西北大学自然科学基金资助项目(No.338020006，No.338050003);西北大学研究生创新项目(No.YZZ15096)
- DOI：10.3788/OPE.20172501.0042
  中图分类号： Q63;TP391
- 收稿日期：2016-06-24，
  
  录用日期：2016-8-12，
  
  纸质出版日期：2017-01-25
- 稿件说明：
移动端阅览
侯榆青, 贾涛, 易黄建, 等. 基于改进谱投影梯度算法的X射线发光断层成像[J]. 光学精密工程, 2017,25(1):42-49.

Yu-qing HOU, Tao JIA, Huang-jian YI, et al. X-ray luminescence computed tomography based on improved spectral projected gradient algorithm[J]. Editorial office of optics and precision engineeri, 2017, 25(1): 42-49.
侯榆青, 贾涛, 易黄建, 等. 基于改进谱投影梯度算法的X射线发光断层成像[J]. 光学精密工程, 2017,25(1):42-49. DOI： 10.3788/OPE.20172501.0042.

Yu-qing HOU, Tao JIA, Huang-jian YI, et al. X-ray luminescence computed tomography based on improved spectral projected gradient algorithm[J]. Editorial office of optics and precision engineeri, 2017, 25(1): 42-49. DOI： 10.3788/OPE.20172501.0042.

摘要

X射线发光断层成像（XLCT）是一种可同时获得解剖结构和功能信息的新型分子影像技术，在早期肿瘤检测与放疗方面具有重要应用潜力，但由于测量信息少，成像模型复杂等原因，其断层重建一直是挑战性难题。本文采用非单调Barzilai-Borwein梯度（NBBG）算法来求解重建问题目标函数。每次迭代中，谱投影梯度方法近似为L

范数约束的最小二乘问题。Barzilai-Borwein梯度法获得相应的更新方向，提高算法的收敛速度。采用非单调性线性搜索策略构建最优步长，保证全局收敛性。通过将Barzilai-Borwein梯度法和非单调性搜索结合，在保证全局收敛的同时，克服了选取精确步长带来较大计算量的缺点。数值仿真实验和物理实验得到的基于NBBG算法的单光原重建位置误差分别为0.68和0.94 mm，与分裂增广拉格朗日收缩算法（SALSA）相比，本文方法在重建精度、鲁棒性和重建效率等方面都获得了较优的结果。

Abstract

X-ray Luminescence Computed Tomography (XLCT)

a novel imaging technique which can obtain anatomical structure and functional information simultaneously

has an important application prospect in early tumor detection and radiotherapy. But due to the less measurement and complex imaging model

the tomography reconstruction always is a challenging problem. This paper presents a gradient algorithm based on Non-monotone Barzilai-Borwein(NBBG) to obtain the optimal solution of the objective. In each iteration

a spectral gradient-projection method approximately was minimized as a least-squares problem with an explicit L

-regularized constraint. The Barzilai-Borwein was employed to get the appropriate updating direction

further to improve the convergence speed of the proposed method. In addition

anonmonotone line search strategy was applied to build the optimal step length

which guarantees global convergence. The combination of nonmonotone line Barzilai-Borwein step length search strategy with spectral projected gradient method not only can ensure the global convergence

but also can reduce the computational cost of selecting exact step-size. From numerical simulation experiments and the physical experiment

the Location Errors(LE) of single target reconstruction based on NBBG are 0.68 and 0.94 mm respectively. Compared with Split Augmented Lagrangian Shrinkage Algorithm(SALSA)

NBBG can obtain better results in terms of LE

robustness and efficiency.

关键词

Keywords

references

张海波,耿国华,易黄建,等. X射线发光断层成像中笔束与锥束激发性能的对比[J]. 光学精密工程, 2016, 24(5):986-992.

ZHANG H B, GENG G H, YI H J, et al.. Comparison between pencil-beam and cone-beam in XLCT system[J]. Opt. Precision Eng., 2016, 24(5):986-992.(in Chinese)

LIU X, WANG H K, YAN Z Z. Chapter 13-Nanobiomaterials in X-ray luminescence computed tomography (XLCT) imaging[M]//GRUMEZESCU A. Nanobiomaterials in Medical Imaging. Amsterdam:Elsevier Inc, 2016.

KUANG Y, PRATX G, BAZALOVA M, et al.. First demonstration of multiplexed X-ray fluorescence computed tomography (XFCT) imaging[J]. IEEE Transactions on Medical Imaging, 2013, 32(2):262-267.

LO P A, LIN M L, JIN S C, et al.. Cone beam x-ray luminescence computed tomography reconstruction with a priori anatomical information[J]. SPIE, 2014, 9205:92050O-92050O-5.

ZHANG W, ZHU D W,LI C Q. Multiple pinhole collimator based microscopic X-ray luminescence computed tomography[J]. SPIE, 2016,9788:9788Q.

PRATX G, CARPENTER C M, SUN C, et al.. Tomographic molecular imaging of x-ray-excitable nanoparticles[J]. Optics Letters, 2010, 35(20):3345-3347.

PRATX G, CARPENTER C M, SUN C, et al.. X-ray luminescence computed tomography via selective excitation:a feasibility study[J]. IEEE Transactions on Medical Imaging, 2010, 29(12):1992-1999.

LI C Q, DÁVALOS A M, CHERRY S R. Numerical and experimental studies of X-ray luminescence optical tomography for small animal imaging[J].SPIE, 2013,8578:85781B-85781B-13.

CHEN D M, ZHU S P, YI H J, et al.. Cone beam X-ray luminescence computed tomography:a feasibility study[J]. Medical Physics, 2013, 40(3):031111.

LIU X,LIAO Q M, WANG H K. In vivo X-ray luminescence tomographic imaging with single-view data[J]. Optics Letters, 2013, 38(22):4530-4533.

KLOSE A D, NTZIACHRISTOS V, HIELSCHER A H. The inverse source problem based on theradiative transfer equation in optical molecular imaging[J].Journal of Computational Physics, 2005, 202(1):323-345.

CHEN D M, ZHU S P, CAO X, et al.. X-ray luminescence computed tomography imaging based on X-ray distribution model and adaptively split Bregman method[J]. Biomedical Optics Express, 2015, 6(7):2649-2663.

GAO P, RONG J Y, PU H SH, et al.. Scattering-compensated cone beam X-ray luminescence computed tomography[J]. SPIE, 2016,9783:978354.

ZHANG W, ZHU D W, ZHANG K, et al.. Microscopic X-ray luminescence computed tomography[J]. SPIE, 2015, 9316:93160M-93160M-6.

LIU X, LIAO Q M, WANG H K. Fast X-ray luminescence computed tomography imaging[J]. IEEE Transactions on Bio-medical Engineering, 2014, 61(6):1621-1627.

ZAMBRANO N M, ARIAS F X. Comparative analysis of sparse signal reconstruction.algorithms for compressed sensing[C]. Twelfth LACCEI Latin American and Caribbean Conference for Engineering and Technology, LACCEI,2014:1-10.

HAN D, YANG X, LIU K, et al.. Efficient reconstruction method for L 1 regularization in fluorescence molecular tomography[J]. Applied Optics, 2010, 49(36):6930-6937.

XIAO Y H, WU S Y, QI L Q. Nonmonotone Barzilai-Borwein gradient algorithm for L 1 -regularized nonsmooth minimization in compressive sensing[J]. Journal of Scientific Computing, 2014, 61(1):17-41.

贺小伟,金晨,易黄建,等. 基于分割增广拉格朗日收缩的X射线发光计算机断层成像[J]. 光学学报, 2016, 36(3):0317001.

HE X W, JIN CH, YI H J, et al.. X-ray luminescence computed tomography based on split augmented lagrangian shrinkage algorithm[J]. Acta Optica Sinica, 2016, 36(3):0317001. (in Chinese)

AFONSO M V, BIOUCASDIAS J M, FIGUEIREDO M A T. Fast image recovery using variable splitting and constrained optimization[J]. IEEE Transactions on Image Processing,2010, 19(9):2345-2356.

BARZILAI J, BORWEIN J M. Two-point step size gradient methods[J]. Ima Journal of Numerical Analysis, 1988, 8(1):141-148..

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