TY - JOUR
T1 - Compressive diffuse optical tomography
T2 - Noniterative exact reconstruction using joint sparsity
AU - Lee, Okkyun
AU - Kim, Jong Min
AU - Bresler, Yoram
AU - Ye, Jong Chul
N1 - Funding Information:
Manuscript received February 10, 2011; accepted February 27, 2011. Date of publication March 10, 2011; date of current version May 04, 2011. This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) under Grant 2010-0000855. The work of Y. Bresler was supported by the U.S. National Science Foundation under Grant CCF-06-35234. Asterisk indicates corresponding author.
Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011/5
Y1 - 2011/5
N2 - Diffuse optical tomography (DOT) is a sensitive and relatively low cost imaging modality that reconstructs optical properties of a highly scattering medium. However, due to the diffusive nature of light propagation, the problem is severely ill-conditioned and highly nonlinear. Even though nonlinear iterative methods have been commonly used, they are computationally expensive especially for three dimensional imaging geometry. Recently, compressed sensing theory has provided a systematic understanding of high resolution reconstruction of sparse objects in many imaging problems; hence, the goal of this paper is to extend the theory to the diffuse optical tomography problem. The main contributions of this paper are to formulate the imaging problem as a joint sparse recovery problem in a compressive sensing framework and to propose a novel noniterative and exact inversion algorithm that achieves the l0 optimality as the rank of measurement increases to the unknown sparsity level. The algorithm is based on the recently discovered generalized MUSIC criterion, which exploits the advantages of both compressive sensing and array signal processing. A theoretical criterion for optimizing the imaging geometry is provided, and simulation results confirm that the new algorithm outperforms the existing algorithms and reliably reconstructs the optical inhomogeneities when we assume that the optical background is known to a reasonable accuracy.
AB - Diffuse optical tomography (DOT) is a sensitive and relatively low cost imaging modality that reconstructs optical properties of a highly scattering medium. However, due to the diffusive nature of light propagation, the problem is severely ill-conditioned and highly nonlinear. Even though nonlinear iterative methods have been commonly used, they are computationally expensive especially for three dimensional imaging geometry. Recently, compressed sensing theory has provided a systematic understanding of high resolution reconstruction of sparse objects in many imaging problems; hence, the goal of this paper is to extend the theory to the diffuse optical tomography problem. The main contributions of this paper are to formulate the imaging problem as a joint sparse recovery problem in a compressive sensing framework and to propose a novel noniterative and exact inversion algorithm that achieves the l0 optimality as the rank of measurement increases to the unknown sparsity level. The algorithm is based on the recently discovered generalized MUSIC criterion, which exploits the advantages of both compressive sensing and array signal processing. A theoretical criterion for optimizing the imaging geometry is provided, and simulation results confirm that the new algorithm outperforms the existing algorithms and reliably reconstructs the optical inhomogeneities when we assume that the optical background is known to a reasonable accuracy.
KW - Diffuse optical tomography (DOT)
KW - generalized MUSIC criterion
KW - joint sparsity
KW - multiple measurement vector (MMV)
KW - p-thresholding
KW - simultaneous orthogonal matching pursuit (S-OMP)
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U2 - 10.1109/TMI.2011.2125983
DO - 10.1109/TMI.2011.2125983
M3 - Article
C2 - 21402507
AN - SCOPUS:79955634911
SN - 0278-0062
VL - 30
SP - 1129
EP - 1142
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 5
M1 - 5728925
ER -