Open AccessSystem configuration optimization for mesoscopic fluorescence molecular tomography
Author(s) -
Fuzheng Yang,
Denzel Faulkner,
Ru Yao,
Mehmet S. Ozturk,
Qinglan Qu,
Xavier Intes
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - Danish
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.005660
Subject(s) - detector , diffuse optical imaging , tomography , computer science , inverse problem , tomographic reconstruction , mesoscopic physics , optics , iterative reconstruction , molecular imaging , fluorescence lifetime imaging microscopy , optical coherence tomography , physics , computer vision , fluorescence , mathematical analysis , mathematics , microbiology and biotechnology , quantum mechanics , in vivo , biology
Tissue engineering applications demand 3D, non-invasive, and longitudinal assessment of bioprinted constructs. Current emphasis is on developing tissue constructs mimicking in vivo conditions; however, these are increasingly challenging to image as they are typically a few millimeters thick and turbid, limiting the usefulness of classical fluorescence microscopic techniques. For such applications, we developed a Mesoscopic Fluorescence Molecular Tomography methodology that collects high information content data to enable high-resolution tomographic reconstruction of fluorescence biomarkers at millimeters depths. This imaging approach is based on an inverse problem; hence, its imaging performances are dependent on critical technical considerations including optode sampling, forward model design and inverse solver parameters. Herein, we investigate the impact of the optical system configuration parameters, including detector layout, number of detectors, combination of detector and source numbers, and scanning mode with uncoupled or coupled source and detector array, on the 3D imaging performances. Our results establish that an MFMT system with a 2D detection chain implemented in a de-scanned mode provides the optimal imaging reconstruction performances.