Purpose: Standard image reconstruction methods for fluorescence Diffuse Optical Tomography
(fDOT) generally make use of L2-regularization. A better choice is to replace the L2 by a total variation
functional that effectively removes noise while preserving edges. Among the wide range of
approaches available, the recently appeared Split Bregman method has been shown to be optimal
and efficient. Furthermore, additional constraints can be easily included. We propose the use of the
Split Bregman ...
Purpose: Standard image reconstruction methods for fluorescence Diffuse Optical Tomography
(fDOT) generally make use of L2-regularization. A better choice is to replace the L2 by a total variation
functional that effectively removes noise while preserving edges. Among the wide range of
approaches available, the recently appeared Split Bregman method has been shown to be optimal
and efficient. Furthermore, additional constraints can be easily included. We propose the use of the
Split Bregman method to solve the image reconstruction problem for fDOT with a nonnegativity
constraint that imposes the reconstructed concentration of fluorophore to be positive.
Methods: The proposed method is tested with simulated and experimental data, and results are
compared with those yielded by an equivalent unconstrained optimization approach based on
Gauss Newton (GN) method, in which the negative part of the solution is projected to zero after
each iteration. In addition, the method dependence on the parameters that weigh data fidelity and
nonnegativity constraints is analyzed.
Results: Split Bregman yielded a reduction of the solution error norm and a better full width at
tenth maximum for simulated data, and higher signal-to-noise ratio for experimental data. It is also
shown that it led to an optimum solution independently of the data fidelity parameter, as long as the
number of iterations is properly selected, and that there is a linear relation between the number of
iterations and the inverse of the data fidelity parameter.
Conclusions: Split Bregman allows the addition of a nonnegativity constraint leading to improve
image quality.
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