[1] 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)

[2] 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.

[3] 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.

[4] 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.

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

[6] 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.

[7] 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.

[8] LI C Q, DVALOS 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.

[9] 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.

[10] 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.

[11] 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.

[12] 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.

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

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

[15] 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.

[16] 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.

[17] HAN D, YANG X, LIU K, et al.. Efficient reconstruction method for L1 regularization in fluorescence molecular tomography ［J］. Applied Optics, 2010, 49(36): 6930-6937.

[18] XIAO Y H, WU S Y, QI L Q. Nonmonotone Barzilai-Borwein gradient algorithm for L1-regularized nonsmooth minimization in compressive sensing ［J］. Journal of Scientific Computing, 2014, 61(1): 17-41.

[19] 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)

[20] 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.

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