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

<bold>Significance: </bold>Treatment planning for light-based therapies including photodynamic therapy requires tissue optical property knowledge. These are recoverable with spatially-resolved diffuse reflectance spectroscopy (DRS), but requires precise source-detector separation (SDS) determination and time-consuming simulations. <bold>Aim: </bold>An artificial neural network (ANN) to map from DRS at short SDS to optical properties was created. This trained ANN was adapted to fiber-optic probes with varying SDS using transfer learning. <bold>Approach: </bold>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 transfer learning algorithm creation. Data from a third were used to test this algorithm. <bold>Results: </bold>The initial ANN recovered absorber concentration with RMSE=0.29 mu M (7.5% mean error) and mu (s) (') at 665 nm (mu (s,665) (') ) with RMSE=0.77 cm (-1) (2.5% mean error). For probe-2, transfer learning significantly improved absorber concentration (0.38 vs. 1.67 mu M, p=0.0005) and mu (s,665) (') (0.71 vs. 1.8 cm (-1) , p=0.0005) recovery. A third probe also showed improved absorber (0.7 vs. 4.1 mu M, p<0.0001) and <mu> (s,665) (') (1.68 vs. 2.08 cm (-1) , p=0.2) recovery. <bold>Conclusions: </bold>A data-driven approach to optical property extraction can be used to rapidly calibrate new fiber-optic probes with varying SDS, with as few as three calibration spectra.