Application of Transfer Learning for Rapid Calibration of Spatially-resolved Diffuse Reflectance Probes for Extraction of Tissue Optical Properties
AbstractO_ST_ABSSignificanceC_ST_ABSTreatment 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. AimAn 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. 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 transfer learning algorithm creation. Data from a third were used to test this algorithm. ResultsThe initial ANN recovered absorber concentration with RMSE=0.29 {micro}M (7.5% mean error) and {micro}s at 665 nm ({micro}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 {micro}M, p=0.0005) and {micro}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 {micro}M, p<0.0001) and {micro}s,665 (1.68 vs. 2.08 cm-1, p=0.2) recovery. ConclusionsA 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.