Greedy algorithms for diffuse optical tomography reconstruction

被引:12
作者
Dileep, B. P. V. [1 ]
Das, Tapan [1 ]
Dutta, Pranab K. [1 ]
机构
[1] IIT Kharagpur, Dept Elect Engn, Kharagpur 721302, W Bengal, India
关键词
Diffuse optical tomography (DOT); Single measurement vector (SMV); Multiple measurement vectors (MMV); Greedy algorithms; SIGNAL RECOVERY; INVERSE PROBLEM; ABSORPTION;
D O I
10.1016/j.optcom.2017.09.056
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Diffuse optical tomography (DOT) is a noninvasive imaging modality that reconstructs the optical parameters of a highly scattering medium. However, the inverse problem of DOT is ill-posed and highly nonlinear due to the zig-zag propagation of photons that diffuses through the cross section of tissue. The conventional DOT imaging methods iteratively compute the solution of forward diffusion equation solver which makes the problem computationally expensive. Also, these methods fail when the geometry is complex. Recently, the theory of compressive sensing (CS) has received considerable attention because of its efficient use in biomedical imaging applications. The objective of this paper is to solve a given DOT inverse problem by using compressive sensing framework and various Greedy algorithms such as orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP), and stagewise orthogonal matching pursuit (StOMP), regularized orthogonal matching pursuit (ROMP) and simultaneous orthogonal matching pursuit (S-OMP) have been studied to reconstruct the change in the absorption parameter i.e, Delta alpha from the boundary data. Also, the Greedy algorithms have been validated experimentally on a paraffin wax rectangular phantom through a well designed experimental set up. We also have studied the conventional DOT methods like least square method and truncated singular value decomposition (TSVD) for comparison. One of the main features of this work is the usage of less number of source-detector pairs, which can facilitate the use of DOT in routine applications of screening. The performance metrics such as mean square error (MSE), normalized mean square error (NMSE), structural similarity index (SSIM), and peak signal to noise ratio (PSNR) have been used to evaluate the performance of the algorithms mentioned in this paper. Extensive simulation results confirm that CS based DOT reconstruction outperforms the conventional DOT imaging methods in terms of computational efficiency. The main advantage of this study is that the forward diffusion equation solver need not be repeatedly solved. (c) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:164 / 173
页数:10
相关论文
共 36 条
  • [1] [Anonymous], 2006, MULTIPLE SCATTERING
  • [2] Arridge S.R., 1991, OPTICS ELECTROOPTICS, V1431, P204
  • [3] Optical tomography: forward and inverse problems
    Arridge, Simon R.
    Schotland, John C.
    [J]. INVERSE PROBLEMS, 2009, 25 (12)
  • [4] Optical tomography in medical imaging
    Arridge, SR
    [J]. INVERSE PROBLEMS, 1999, 15 (02) : R41 - R93
  • [5] IEEE-SPS and connexions - An open access education collaboration
    Baraniuk, Richard G.
    Burrus, C. Sidney
    Thierstein, E. Joel
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2007, 24 (06) : 6 - +
  • [6] Beuthan J., 2007, Med. Laser Appl, V22, P43, DOI [10.1016/j.mla.2007.02.001, DOI 10.1016/J.MLA.2007.02.001]
  • [7] Imaging the body with diffuse optical tomography
    Boas, DA
    Brooks, DH
    Miller, EL
    DiMarzio, CA
    Kilmer, M
    Gaudette, RJ
    Zhang, Q
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2001, 18 (06) : 57 - 75
  • [8] Robust uncertainty principles:: Exact signal reconstruction from highly incomplete frequency information
    Candès, EJ
    Romberg, J
    Tao, T
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (02) : 489 - 509
  • [9] Theoretical results on sparse representations of multiple-measurement vectors
    Chen, Jie
    Huo, Xiaoming
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2006, 54 (12) : 4634 - 4643
  • [10] Donoho D., NEIGHBORLY POLYTOPES