Biomedical optical tomography using dynamic parameterization and bayesian conditioning on photon migration...

Area of Science:

• Biomedical Optics
• Computational Imaging
• Photon Migration Imaging

Background:

• Accurate mapping of subsurface tissue optical properties is crucial for biomedical imaging.
• Frequency-domain photon migration (FDPM) offers a non-invasive approach to probe tissue interiors.
• Existing reconstruction methods often face challenges with accuracy, stability, and computational efficiency.

Purpose of the Study:

• To develop and validate stochastic reconstruction techniques for mapping interior tissue optical properties.
• To accurately reconstruct parameter fields such as absorption cross-section and fluorescence lifetime.
• To enhance the accuracy, stability, and computational efficiency of inversion methods for FDPM.

Main Methods:

• Utilized a recursive, Bayesian, minimum-variance estimator (approximate extended Kalman filter) for parameter field reconstruction.
• Employed data-driven zonation following parameter field updates to improve system conditioning.
• Modeled parameters as beta-distributed random variables to constrain estimates within feasible ranges, avoiding arbitrary smoothing or regularization.

Main Results:

• Accurate reconstruction of absorption cross-section, fluorescence lifetime, and quantum efficiency from simulated noisy FDPM measurements.
• Demonstrated improved accuracy, stability, and computational efficiency through data-driven zonation.
• Bayesian reconstructions showed comparable speed and accuracy to Newton-Raphson-based inversions.

Conclusions:

• The developed Bayesian stochastic reconstruction technique is effective for mapping tissue optical properties.
• The method shows promise for advancing three-dimensional biomedical imaging applications using FDPM.
• The approach offers a robust and efficient alternative to traditional inversion techniques.