A three-dimensional finite element model and image reconstruction algorithm for time-domain fluorescence imaging in...

Area of Science:

• Biomedical Optics
• Computational Modeling
• Medical Imaging

Background:

• Near-infrared (NIR) fluorescence imaging is valuable for deep tissue diagnostics.
• Accurate modeling of light transport is crucial for quantitative imaging.
• Existing models may struggle with complex geometries and heterogeneous tissues.

Purpose of the Study:

• To develop and validate a 3D finite element model (FEM) for time-domain (TD) NIR fluorescence light transport.
• To assess the model's accuracy against analytical and Monte Carlo (MC) methods.
• To evaluate the impact of photon arrival times on image reconstruction accuracy.

Main Methods:

• Development of a 3D FEM based on the diffusion approximation for TD NIR fluorescence.
• Generation of excitation and fluorescence temporal point-spread function (TPSF) data.
• Validation against analytical solutions and MC simulations.
• Image reconstruction framework utilizing FEM and time-gated photon data.

Main Results:

• The TD FEM achieved high quantitative accuracy (<0.72% intensity error, <37 ps mean time error).
• Born-Ratio normalization reduced FEM-MC data mismatch (<0.22% intensity, <22 ps mean time).
• Using early photons (<200 ps) improved spatial resolution compared to continuous-wave signals.
• Employing early and late time gates enhanced spatial resolution and contrast recovery.

Conclusions:

• The developed 3D FEM provides accurate simulation of NIR fluorescence light transport in complex biological tissues.
• Time-domain analysis, particularly using early photons, significantly enhances image reconstruction quality.
• The model and reconstruction framework show promise for quantitative fluorescence imaging in biomedical applications.