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Singular value decomposition based computationally efficient algorithm for rapid dynamic near‐infrared diffuse optical tomography
Author(s) -
Gupta Saurabh,
Yalavarthy Phaneendra K.,
Roy Debasish,
Piao Daqing,
Vasu Ram M.
Publication year - 2009
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3261029
Subject(s) - singular value decomposition , algorithm , iterative reconstruction , iterative method , diffuse optical imaging , jacobian matrix and determinant , computer science , nonlinear system , image quality , mathematics , image (mathematics) , artificial intelligence , physics , quantum mechanics
Purpose: A computationally efficient algorithm (linear iterative type) based on singular value decomposition (SVD) of the Jacobian has been developed that can be used in rapid dynamic near‐infrared (NIR) diffuse optical tomography. Methods: Numerical and experimental studies have been conducted to prove the computational efficacy of this SVD‐based algorithm over conventional optical image reconstruction algorithms. Results: These studies indicate that the performance of linear iterative algorithms in terms of contrast recovery (quantitation of optical images) is better compared to nonlinear iterative (conventional) algorithms, provided the initial guess is close to the actual solution. The nonlinear algorithms can provide better quality images compared to the linear iterative type algorithms. Moreover, the analytical and numerical equivalence of the SVD‐based algorithm to linear iterative algorithms was also established as a part of this work. It is also demonstrated that the SVD‐based image reconstruction typically requires O ( N N 2 )operations per iteration, as contrasted with linear and nonlinear iterative methods that, respectively, require O ( N N 3 )and O ( N N 6 )operations, with “ N N ” being the number of unknown parameters in the optical image reconstruction procedure. Conclusions: This SVD‐based computationally efficient algorithm can make the integration of image reconstruction procedure with the data acquisition feasible, in turn making the rapid dynamic NIR tomography viable in the clinic to continuously monitor hemodynamic changes in the tissue pathophysiology.