Robust Reconstruction of Fluorescence Molecular Tomography Based on Sparsity Adaptive Correntropy Matching Pursuit Method for Stem Cell Distribution

Fluorescence molecular tomography (FMT), as a promising imaging modality in preclinical research, can obtain the three-dimensional (3-D) position information of the stem cell in mice. However, because of the ill-posed nature and sensitivity to noise of the inverse problem, it is a challenge to develop a robust reconstruction method, which can accurately locate the stem cells and define the distribution. In this paper, we proposed a sparsity adaptive correntropy matching pursuit (SACMP) method. SACMP method is independent on the noise distribution of measurements and it assigns small weights on severely corrupted entries of data and large weights on clean ones adaptively. These properties make it more suitable for in vivo experiment. To analyze the performance in terms of robustness and practicability of SACMP, we conducted numerical simulation and in vivo mice experiments. The results demonstrated that the SACMP method obtained the highest robustness and accuracy in locating stem cells and depicting stem cell distribution compared with stagewise orthogonal matching pursuit and sparsity adaptive subspace pursuit reconstruction methods. To the best of our knowledge, this is the first study that acquired such accurate and robust FMT distribution reconstruction for stem cell tracking in mice brain. This promotes the application of FMT in locating stem cell and distribution reconstruction in practical mice brain injury models.

[1]  Michael Elad,et al.  Coordinate and subspace optimization methods for linear least squares with non-quadratic regularization , 2007 .

[2]  R. Raghupathi,et al.  Factors affecting increased risk for substance use disorders following traumatic brain injury: What we can learn from animal models , 2017, Neuroscience & Biobehavioral Reviews.

[3]  Yan Zhang,et al.  Tracking of Transplanted Human Mesenchymal Stem Cells in Living Mice using Near‐Infrared Ag2S Quantum Dots , 2014 .

[4]  A. Adibi,et al.  Optimal sparse solution for fluorescent diffuse optical tomography: theory and phantom experimental results. , 2007, Applied optics.

[5]  Ben Zhong Tang,et al.  Ultrabright organic dots with aggregation-induced emission characteristics for cell tracking. , 2014, Biomaterials.

[6]  Weifeng Liu,et al.  Correntropy: Properties and Applications in Non-Gaussian Signal Processing , 2007, IEEE Transactions on Signal Processing.

[7]  Bao-Gang Hu,et al.  Robust feature extraction via information theoretic learning , 2009, ICML '09.

[8]  Lu Zhang,et al.  MRI/SPECT/Fluorescent Tri‐Modal Probe for Evaluating the Homing and Therapeutic Efficacy of Transplanted Mesenchymal Stem Cells in a Rat Ischemic Stroke Model , 2015, Advanced functional materials.

[9]  Xin Yang,et al.  Enhanced immunotherapy of SM5-1 in hepatocellular carcinoma by conjugating with gold nanoparticles and its in vivo bioluminescence tomographic evaluation. , 2016, Biomaterials.

[10]  Vasilis Ntziachristos,et al.  Fluorescence molecular tomography of DiR-labeled mesenchymal stem cell implants for osteochondral defect repair in rabbit knees , 2017, European Radiology.

[11]  Jie Tian,et al.  Efficient reconstruction method for L1 regularization in fluorescence molecular tomography. , 2010, Applied optics.

[12]  Jie Tian,et al.  Galerkin-based meshless methods for photon transport in the biological tissue. , 2008, Optics express.

[13]  Jie Tian,et al.  A fast reconstruction algorithm for fluorescence molecular tomography with sparsity regularization. , 2010, Optics express.

[14]  Dong Han,et al.  Sparsity-Promoting Tomographic Fluorescence Imaging With Simplified Spherical Harmonics Approximation , 2010, IEEE Transactions on Biomedical Engineering.

[15]  V. Ntziachristos Going deeper than microscopy: the optical imaging frontier in biology , 2010, Nature Methods.

[16]  Hongbo Liu,et al.  In vivo pentamodal tomographic imaging for small animals. , 2017, Biomedical optics express.

[17]  Jie Tian,et al.  A Novel Region Reconstruction Method for Fluorescence Molecular Tomography , 2015, IEEE Transactions on Biomedical Engineering.

[18]  Svetha Venkatesh,et al.  Improved Image Recovery From Compressed Data Contaminated With Impulsive Noise , 2012, IEEE Transactions on Image Processing.

[19]  Mila Nikolova,et al.  Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery , 2005, SIAM J. Sci. Comput..

[20]  Huan-Cheng Chang,et al.  Tracking the Engraftment and Regenerative Capabilities of Transplanted Lung Stem Cells using Fluorescent Nanodiamonds , 2014 .

[21]  S. Jacques Optical properties of biological tissues: a review , 2013, Physics in medicine and biology.

[22]  V. Ntziachristos Fluorescence molecular imaging. , 2006, Annual review of biomedical engineering.

[23]  Nanguang Chen,et al.  Reconstruction for free-space fluorescence tomography using a novel hybrid adaptive finite element algorithm. , 2007, Optics express.

[24]  Johan A. K. Suykens,et al.  Learning with the maximum correntropy criterion induced losses for regression , 2015, J. Mach. Learn. Res..

[25]  V. Ntziachristos,et al.  FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography–X-ray computed tomography , 2012, Nature Methods.

[26]  Yuan Yan Tang,et al.  Correntropy Matching Pursuit With Application to Robust Digit and Face Recognition , 2017, IEEE Transactions on Cybernetics.

[27]  R. Leahy,et al.  Digimouse: a 3D whole body mouse atlas from CT and cryosection data , 2007, Physics in medicine and biology.

[28]  Jie Tian,et al.  Reconstruction of fluorescence molecular tomography via a nonmonotone spectral projected gradient pursuit method , 2014, Journal of biomedical optics.

[29]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[30]  Xin Liu,et al.  A Novel Finite-Element-Based Algorithm for Fluorescence Molecular Tomography of Heterogeneous Media , 2009, IEEE Transactions on Information Technology in Biomedicine.

[31]  Arye Nehorai,et al.  Image reconstruction for diffuse optical tomography using sparsity regularization and expectation-maximization algorithm. , 2007, Optics express.

[32]  Wolfgang Bangerth,et al.  Adaptive finite element methods for the solution of inverse problems in optical tomography , 2008 .

[33]  Ge Wang,et al.  A finite-element-based reconstruction method for 3D fluorescence tomography. , 2005, Optics express.

[34]  Jie Tian,et al.  Fast and robust reconstruction for fluorescence molecular tomography via a sparsity adaptive subspace pursuit method. , 2014, Biomedical optics express.

[35]  Fenghua Tian,et al.  Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography , 2012, Biomedical optics express.

[36]  Gang Liu,et al.  Functional quantum dot-siRNA nanoplexes to regulate chondrogenic differentiation of mesenchymal stem cells. , 2016, Acta biomaterialia.

[37]  Bin Cao,et al.  Highly fluorescent and bioresorbable polymeric nanoparticles with enhanced photostability for cell imaging. , 2015, Nanoscale.

[38]  Deniz Erdogmus,et al.  An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems , 2002, IEEE Trans. Signal Process..