A wavelet-based single-view reconstruction approach for cone beam x-ray luminescence tomography imaging.

Single-view x-ray luminescence computed tomography (XLCT) imaging has short data collection time that allows non-invasively and fast resolving the three-dimensional (3-D) distribution of x-ray-excitable nanophosphors within small animal in vivo. However, the single-view reconstruction suffers from a severe ill-posed problem because only one angle data is used in the reconstruction. To alleviate the ill-posedness, in this paper, we propose a wavelet-based reconstruction approach, which is achieved by applying a wavelet transformation to the acquired singe-view measurements. To evaluate the performance of the proposed method, in vivo experiment was performed based on a cone beam XLCT imaging system. The experimental results demonstrate that the proposed method cannot only use the full set of measurements produced by CCD, but also accelerate image reconstruction while preserving the spatial resolution of the reconstruction. Hence, it is suitable for dynamic XLCT imaging study.

[1]  Vasilis Ntziachristos,et al.  Looking and listening to light: the evolution of whole-body photonic imaging , 2005, Nature Biotechnology.

[2]  David L. Donoho,et al.  Sparse Solution Of Underdetermined Linear Equations By Stagewise Orthogonal Matching Pursuit , 2006 .

[3]  Timothy J Rudge,et al.  Full-wavelet approach for fluorescence diffuse optical tomography with structured illumination. , 2010, Optics letters.

[4]  Jie Tian,et al.  A multilevel adaptive finite element algorithm for bioluminescence tomography. , 2006, Optics express.

[5]  Jianwen Luo,et al.  4-D Reconstruction for Dynamic Fluorescence Diffuse Optical Tomography , 2012, IEEE Transactions on Medical Imaging.

[6]  M. Schweiger,et al.  The finite element method for the propagation of light in scattering media: boundary and source conditions. , 1995, Medical physics.

[7]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[8]  J. Tropp,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.

[9]  Mike E. Davies,et al.  Gradient Pursuits , 2008, IEEE Transactions on Signal Processing.

[10]  Jean-Luc Starck,et al.  Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit , 2012, IEEE Transactions on Information Theory.

[11]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[12]  Gianluca Valentini,et al.  Fluorescence molecular tomography of an animal model using structured light rotating view acquisition , 2013, Journal of biomedical optics.

[13]  Richard Perron,et al.  Eu3+-doped Gd2O3 nanoparticles as reporters for optical detection and visualization of antibodies patterned by microcontact printing , 2006, Analytical and bioanalytical chemistry.

[14]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[15]  Vasilis Ntziachristos,et al.  Performance dependence of hybrid x-ray computed tomography/fluorescence molecular tomography on the optical forward problem. , 2009, Journal of the Optical Society of America. A, Optics, image science, and vision.

[16]  Julien Bec,et al.  X-ray luminescence optical tomography imaging: experimental studies. , 2013, Optics letters.

[17]  Jie Tian,et al.  Cone beam x-ray luminescence computed tomography: a feasibility study. , 2013, Medical physics.

[18]  Hongkai Wang,et al.  Estimation of Mouse Organ Locations Through Registration of a Statistical Mouse Atlas With Micro-CT Images , 2012, IEEE Transactions on Medical Imaging.

[19]  Ana Maria Pires,et al.  The effect of Eu 3+ ion doping concentration in Gd 2O 3 fine spherical particles , 2002 .

[20]  Pierre Vandergheynst,et al.  Dictionary Preconditioning for Greedy Algorithms , 2008, IEEE Transactions on Signal Processing.

[21]  Timothy J Rudge,et al.  Fast image reconstruction in fluorescence optical tomography using data compression. , 2010, Optics letters.

[22]  Vasilis Ntziachristos,et al.  Preconditioning of the fluorescence diffuse optical tomography sensing matrix based on compressive sensing. , 2012, Optics letters.

[23]  E. Hoffman,et al.  In vivo mouse studies with bioluminescence tomography. , 2006, Optics express.

[24]  Lei Xing,et al.  Tomographic molecular imaging of x-ray-excitable nanoparticles. , 2010, Optics letters.

[25]  Lei Xing,et al.  X-Ray Luminescence Computed Tomography via Selective Excitation: A Feasibility Study , 2010, IEEE Transactions on Medical Imaging.

[26]  Qimei Liao,et al.  In vivo x-ray luminescence tomographic imaging with single-view data. , 2013, Optics letters.

[27]  Wenxiang Cong,et al.  Spectrally resolving and scattering-compensated x-ray luminescence/fluorescence computed tomography. , 2011, Journal of biomedical optics.

[28]  Alain Brenier,et al.  Synthesis and luminescent properties of sub-5-nm lanthanide oxides nanoparticles , 2003 .