2016
DOI: 10.1109/tbme.2015.2483539
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Iterative Correction Scheme Based on Discrete Cosine Transform and L1 Regularization for Fluorescence Molecular Tomography With Background Fluorescence

Abstract: The influence of background fluorescence in FMT can be reduced effectively because of the filtering of the intermediate results, the detail preservation, and noise suppression of L1 regularization.

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Cited by 14 publications
(6 citation statements)
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“…In this section, we studied an optimal strategy of network building for further application in practice. The accuracy of our method was quantified by the relative error [23] (RE) and the Dice coefficient [24] (DC). The RE, defined as equation (4), represents the difference between reconstruction and true source.…”
Section: Analysis Of Network Architecturesmentioning
confidence: 99%
“…In this section, we studied an optimal strategy of network building for further application in practice. The accuracy of our method was quantified by the relative error [23] (RE) and the Dice coefficient [24] (DC). The RE, defined as equation (4), represents the difference between reconstruction and true source.…”
Section: Analysis Of Network Architecturesmentioning
confidence: 99%
“…A small signal disturbance may lead to a large reconstruction error. Therefore, researchers apply regularization techniques to FMT reconstruction to constrain the reconstruction process and reduce morbidity [8,9,28,30,63,[98][99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115]. The main principle of regularization is as follows:…”
Section: Inverse Problem Solvingmentioning
confidence: 99%
“…L 1 norm regularization (p = 1) is the mainstream sparse reconstruction method applied to FMT reconstruction today, which can reconstruct a good fluorescence three-dimensional distribution image based on less fluorescence acquisition information [10,98,108,109,113]. Numerical simulation, physical simulation and in vivo experiments verify that regularization of L 1 norm and L 2 norm regularization can achieve more accurate reconstruction results [113]. However, L 1 norm regularization works well in reconstructing a sparse light source, but over-convergence also exists.…”
Section: Inverse Problem Solvingmentioning
confidence: 99%
“…But the simulated background cannot reflect the true optical heterogeneity, especially after nanoparticles are administrated. Furthermore, some regularization-based reconstruction algorithms were also investigated to mitigate the influences of background fluorescence on the FMT reconstruction ( 26 , 27 ). But these algorithms only suitable for the scenarios wherein the background fluorescence is far weaker than the target fluorescence.…”
Section: Introductionmentioning
confidence: 99%