Total variation regularization for bioluminescence tomography with the split Bregman method

被引：38

作者：

Feng, Jinchao ^{[1
]}

Qin, Chenghu ^{[2
]}

Jia, Kebin ^{[1
]}

Zhu, Shouping ^{[3
]}

Liu, Kai ^{[2
]}

Han, Dong ^{[2
]}

Yang, Xin ^{[2
]}

Gao, Quansheng ^{[3
]}

Tian, Jie ^{[2
,4
]}

机构：

[1] Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China

[2] Chinese Acad Sci, Inst Automat, Intelligent Med Res Ctr, Beijing 100190, Peoples R China

[3] Acad Mil Med Sci, Lab Anim Ctr, Beijing 100850, Peoples R China

[4] Xidian Univ, Life Sci Res Ctr, Xian 710071, Peoples R China

来源：

APPLIED OPTICS | 2012年 / 51卷 / 19期

基金：

中国国家自然科学基金;

关键词：

TOTAL-VARIATION MINIMIZATION; IMAGE-RECONSTRUCTION; OPTICAL TOMOGRAPHY; SIMULATION; ALGORITHMS; STRATEGY;

D O I：

10.1364/AO.51.004501

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Regularization methods have been broadly applied to bioluminescence tomography (BLT) to obtain stable solutions, including l(2) and l(1) regularizations. However, l(2) regularization can oversmooth reconstructed images and l(1) regularization may sparsify the source distribution, which degrades image quality. In this paper, the use of total variation (TV) regularization in BLT is investigated. Since a nonnegativity constraint can lead to improved image quality, the nonnegative constraint should be considered in BLT. However, TV regularization with a nonnegativity constraint is extremely difficult to solve due to its nondifferentiability and nonlinearity. The aim of this work is to validate the split Bregman method to minimize the TV regularization problem with a nonnegativity constraint for BLT. The performance of split Bregman-resolved TV (SBRTV) based BLT reconstruction algorithm was verified with numerical and in vivo experiments. Experimental results demonstrate that the SBRTV regularization can provide better regularization quality over l(2) and l(1) regularizations. (c) 2012 Optical Society of America

引用

页码：4501 / 4512

页数：12

共 37 条

[1] Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography [J].

Ahn, Sangtae ;

Chaudhari, Abhijit J. ;

Darvas, Felix ;

Bouman, Charles A. ;

Leahy, Richard M. .

PHYSICS IN MEDICINE AND BIOLOGY, 2008, 53 (14) :3921-3942

[2] Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study [J].

Alexandrakis, G ;

Rannou, FR ;

Chatziioannou, AF .

PHYSICS IN MEDICINE AND BIOLOGY, 2005, 50 (17) :4225-4241

[3] CONVERGENCE OF THE LINEARIZED BREGMAN ITERATION FOR l1-NORM MINIMIZATION [J].

Cai, Jian-Feng ;

Osher, Stanley ;

Shen, Zuowei .

MATHEMATICS OF COMPUTATION, 2009, 78 (268) :2127-2136

[4] LINEARIZED BREGMAN ITERATIONS FOR COMPRESSED SENSING [J].

Cai, Jian-Feng ;

Osher, Stanley ;

Shen, Zuowei .

MATHEMATICS OF COMPUTATION, 2009, 78 (267) :1515-1536

[5]

Chambolle A, 2004, J MATH IMAGING VIS, V20, P89

[6] Total variation blind deconvolution [J].

Chan, TF ;

Wong, CK .

IEEE TRANSACTIONS ON IMAGE PROCESSING, 1998, 7 (03) :370-375

[7] Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging [J].

Chaudhari, AJ ;

Darvas, F ;

Bading, JR ;

Moats, RA ;

Conti, PS ;

Smith, DJ ;

Cherry, SR ;

Leahy, RM .

PHYSICS IN MEDICINE AND BIOLOGY, 2005, 50 (23) :5421-5441

[8] A reflective Newton method for minimizing a quadratic function subject to bounds on some of the variables [J].

Coleman, TF ;

Li, YY .

SIAM JOURNAL ON OPTIMIZATION, 1996, 6 (04) :1040-1058

[9] Advances in vivo bioluminescence imaging of gene expression [J].

Contag, CH ;

Bachmann, MH .

ANNUAL REVIEW OF BIOMEDICAL ENGINEERING, 2002, 4 :235-260

[10] Spectrally resolved bioluminescence optical tomography [J].

Dehghani, H ;

Davis, SC ;

Jiang, SD ;

Pogue, BW ;

Paulsen, KD ;

Patterson, MS .

OPTICS LETTERS, 2006, 31 (03) :365-367

← 1 2 3 4 →