Open AccessImproving mesoscopic fluorescence molecular tomography through data reduction
Author(s) -
Fuzheng Yang,
Mehmet S. Ozturk,
Ru Yao,
Xavier Intes
Publication year - 2017
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.8.003868
Subject(s) - imaging phantom , computer science , robustness (evolution) , mesoscopic physics , principal component analysis , computation , inverse problem , sensitivity (control systems) , biological system , image resolution , molecular imaging , image quality , reduction (mathematics) , algorithm , signal to noise ratio (imaging) , optics , artificial intelligence , physics , mathematics , chemistry , electronic engineering , image (mathematics) , mathematical analysis , engineering , biology , biochemistry , geometry , microbiology and biotechnology , quantum mechanics , in vivo , gene , telecommunications
Mesoscopic fluorescence molecular tomography (MFMT) is a novel imaging technique that aims at obtaining the 3-D distribution of molecular probes inside biological tissues at depths of a few millimeters. To achieve high resolution, around 100-150μm scale in turbid samples, dense spatial sampling strategies are required. However, a large number of optodes leads to sizable forward and inverse problems that can be challenging to compute efficiently. In this work, we propose a two-step data reduction strategy to accelerate the inverse problem and improve robustness. First, data selection is performed via signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) criteria. Then principal component analysis (PCA) is applied to further reduce the size of the sensitivity matrix. We perform numerical simulations and phantom experiments to validate the effectiveness of the proposed strategy. In both in silico and in vitro cases, we are able to significantly improve the quality of MFMT reconstructions while reducing the computation times by close to a factor of two.