Application of Transfer Learning for Rapid Calibration of Spatially Resolved Diffuse Reflectance Probes for Extraction of Tissue Optical Properties

Significance: Treatment planning for light-based therapies including photodynamic therapy requires tissue optical property knowledge. This is recoverable with spatially resolved diffuse reflectance spectroscopy (DRS) but requires precise source-detector separation (SDS) determination and time-consuming simulations.

Aim: An artificial neural network (ANN) to map from DRS at multiple SDS to optical properties was created. This trained ANN was adapted to fiber-optic probes with varying SDS using transfer learning (TL).

Approach: An ANN mapping from measurements to Monte Carlo simulation to optical properties was created with one fiber-optic probe. A second probe with different SDS was used for TL algorithm creation. Data from a third were used to test this algorithm.

Results: The initial ANN recovered absorber concentration with (7.5% mean error) and at 665 nm () with (2.5% mean error). For probe 2, TL significantly improved absorber concentration (0.38 versus RMSE, ) and (0.71 versus RMSE, ) recovery. A third probe also showed improved absorber (0.7 versus RMSE, ) and (1.68 versus RMSE, ) recovery.

Conclusions: TL-based probe-to-probe calibration can rapidly adapt an ANN created for one probe to similar target probes, enabling accurate optical property recovery with the target probe.