Abstract
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.
Highlights
Tissue engineering applications have known an explosive growth over the last decades
We have developed efficient Monte Carlo tools to model accurately the sub-diffuse and diffuse regime [21,22], compressive sensing based inverse solvers [23], pre-conditioning techniques to reduce the ill-posedness of the sensitivity matrix [24,25], and data reduction techniques to facilitate the computation of the inverse problem [26]
As in Diffuse Optical Tomography (DOT) or Fluorescence Molecular Tomography (FMT), the detector configuration and density are central to Mesoscopic Fluorescence Molecular Tomography (MFMT) reconstruction fidelity
Summary
Tissue engineering applications have known an explosive growth over the last decades. The non-invasive imaging of fluorescence biomarkers that can be associated with the vascular network as well as numerous cellular functions such as cell viability, proliferation and migration for instance, is extremely desirable. Its imaging performances are highly dependent on the selection of an appropriate forward model, dense spatial measurements and a dedicated inverse solver. To this end, we have developed efficient Monte Carlo tools to model accurately the sub-diffuse and diffuse regime [21,22], compressive sensing based inverse solvers [23], pre-conditioning techniques to reduce the ill-posedness of the sensitivity matrix [24,25], and data reduction techniques to facilitate the computation of the inverse problem [26].
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