Fluorescence diffuse optical tomography using the split Bregman method.

Area of Science:

• Biomedical Imaging
• Optical Physics
• Computational Science

Background:

• Standard fluorescence diffuse optical tomography (fDOT) image reconstruction often uses L2-regularization, which can be suboptimal for noise reduction and edge preservation.
• Total variation (TV) functionals offer superior noise removal and edge preservation compared to L2-regularization.
• The Split Bregman method is an efficient and optimal approach for solving optimization problems with TV functionals and additional constraints.

Purpose of the Study:

• To propose and evaluate the Split Bregman method for fDOT image reconstruction.
• To incorporate a nonnegativity constraint within the Split Bregman framework for fDOT.
• To improve the accuracy and quality of reconstructed fDOT images by ensuring positive fluorophore concentrations.

Main Methods:

• The Split Bregman method was applied to the fDOT image reconstruction problem.
• A nonnegativity constraint was integrated into the Split Bregman optimization.
• Performance was assessed using simulated and experimental fDOT data.
• Results were compared against an unconstrained Gauss-Newton (GN) method with projection.

Main Results:

• Split Bregman method demonstrated reduced solution error norm and improved full width at tenth maximum for simulated data.
• Higher signal-to-noise ratio was achieved for experimental data using Split Bregman.
• The method's solutions were found to be independent of the data fidelity parameter with proper iteration selection.
• A linear relationship was identified between the number of iterations and the inverse of the data fidelity parameter.

Conclusions:

• The Split Bregman method effectively incorporates a nonnegativity constraint in fDOT image reconstruction.
• This integration leads to significant improvements in overall image quality.
• The proposed method offers a robust and efficient solution for advanced fDOT imaging.