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 RMSE = 0.29 μM (7.5% mean error) and μ's at 665 nm (μ's,665) with RMSE = 0.77 cm-1 (2.5% mean error). For probe 2, TL significantly improved absorber concentration (0.38 versus 1.67 μM RMSE, p = 0.0005) and μ's,665 (0.71 versus 1.8 cm-1 RMSE, p = 0.0005) recovery. A third probe also showed improved absorber (0.7 versus 4.1 μM RMSE, p < 0.0001) and μ's,665 (1.68 versus 2.08 cm-1 RMSE, p = 0.2) 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. [ABSTRACT FROM AUTHOR]