Joint L1 and total variation regularization for fluorescence molecular tomography

被引:120
作者
Dutta, Joyita [1 ,2 ]
Ahn, Sangtae [1 ]
Li, Changqing [3 ]
Cherry, Simon R. [3 ]
Leahy, Richard M. [1 ]
机构
[1] Univ So Calif, Dept Elect Engn Syst, Inst Signal & Image Proc, Los Angeles, CA 90089 USA
[2] Massachusetts Gen Hosp, Div Nucl Med & Mol Imaging, Boston, MA 02114 USA
[3] Univ Calif Davis, Dept Biomed Engn, Davis, CA 95616 USA
关键词
RADIATIVE-TRANSFER EQUATION; DIFFUSE OPTICAL TOMOGRAPHY; TOTAL VARIATION MINIMIZATION; IMAGE-RECONSTRUCTION; THRESHOLDING ALGORITHM; MOUSE ATLAS; EMISSION; SPARSE; OPTIMIZATION; SHRINKAGE;
D O I
10.1088/0031-9155/57/6/1459
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Fluorescence molecular tomography (FMT) is an imaging modality that exploits the specificity of fluorescent biomarkers to enable 3D visualization of molecular targets and pathways in vivo in small animals. Owing to the high degree of absorption and scattering of light through tissue, the FMT inverse problem is inherently ill-conditioned making image reconstruction highly susceptible to the effects of noise and numerical errors. Appropriate priors or penalties are needed to facilitate reconstruction and to restrict the search space to a specific solution set. Typically, fluorescent probes are locally concentrated within specific areas of interest (e. g., inside tumors). The commonly used L-2 norm penalty generates the minimum energy solution, which tends to be spread out in space. Instead, we present here an approach involving a combination of the L-1 and total variation norm penalties, the former to suppress spurious background signals and enforce sparsity and the latter to preserve local smoothness and piecewise constancy in the reconstructed images. We have developed a surrogate-based optimization method for minimizing the joint penalties. The method was validated using both simulated and experimental data obtained from a mouse-shaped phantom mimicking tissue optical properties and containing two embedded fluorescent sources. Fluorescence data were collected using a 3D FMT setup that uses an EMCCD camera for image acquisition and a conical mirror for full-surface viewing. A range of performance metrics was utilized to evaluate our simulation results and to compare our method with the L-1, L-2 and total variation norm penalty-based approaches. The experimental results were assessed using the Dice similarity coefficients computed after co-registration with a CT image of the phantom.
引用
收藏
页码:1459 / 1476
页数:18
相关论文
共 62 条
[1]   Globally convergent image reconstruction for emission tomography using relaxed ordered subsets algorithms [J].
Ahn, S ;
Fessler, JA .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2003, 22 (05) :613-626
[2]  
[Anonymous], 1999, Athena scientific Belmont
[3]   Spatially varying regularization based on spectrally resolved fluorescence emission in fluorescence molecular tomography [J].
Axelsson, Johan ;
Svensson, Jenny ;
Andersson-Engels, Stefan .
OPTICS EXPRESS, 2007, 15 (21) :13574-13584
[4]   A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [J].
Beck, Amir ;
Teboulle, Marc .
SIAM JOURNAL ON IMAGING SCIENCES, 2009, 2 (01) :183-202
[5]   Real-time diffuse optical tomography based on structured illumination [J].
Belanger, Samuel ;
Abran, Maxime ;
Intes, Xavier ;
Casanova, Christian ;
Lesage, Frederic .
JOURNAL OF BIOMEDICAL OPTICS, 2010, 15 (01)
[6]   Whole-body fluorescence lifetime imaging of a tumor-targeted near-infrared molecular probe in mice [J].
Bloch, S ;
Lesage, F ;
McIntosh, L ;
Gandjbakhche, A ;
Liang, KX ;
Achilefu, S .
JOURNAL OF BIOMEDICAL OPTICS, 2005, 10 (05)
[7]  
Candès EJ, 2008, IEEE SIGNAL PROC MAG, V25, P21, DOI 10.1109/MSP.2007.914731
[8]   Image reconstruction for diffuse optical tomography using sparsity regularization and expectation-maximization algorithm [J].
Cao, Nannan ;
Nehorai, Arye ;
Jacob, Mathews .
OPTICS EXPRESS, 2007, 15 (21) :13695-13708
[9]  
Chambolle A, 2004, J MATH IMAGING VIS, V20, P89
[10]  
Chan T. F., 1996, ICAOS'96. 12th International Conference on Analysis and Optimization of Systems. Images, Wavelets and PDEs, P241