Application of transfer learning for rapid calibration of spatially resolved diffuse reflectance probes for extraction of tissue optical properties

SignificanceTreatment 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.AimAn 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).ApproachAn 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.ResultsThe initial ANN recovered absorber concentration with RMSE=0.29 μM (7.5% mean error) and μs′ at 665 nm (μs,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 μs,665′ (1.68 versus 2.08 cm−1 RMSE, p=0.2) recovery.ConclusionsTL-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.