Neural network-based optimization of sub-diffuse reflectance spectroscopy for improved parameter prediction and efficient data collection

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    μ M (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    μ M 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    μ M 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.

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. These are 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 short SDS to optical properties was created. This trained ANN was adapted to fiber-optic probes with varying SDS using transfer learning.

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 transfer learning 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, transfer learning significantly improved absorber concentration (0.38 vs. 1.67 µM, p=0.0005) and µ 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 µM, p<0.0001) and µ s,665 ' (1.68 vs. 2.08 cm -1 , p=0.2) recovery.

Conclusions

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.

Neural network-based inverse model for diffuse reflectance spectroscopy

In diffuse reflectance spectroscopy, the retrieval of the optical properties of a target requires the inversion of a measured reflectance spectrum. This is typically achieved through the use of forward models such as diffusion theory or Monte Carlo simulations, which are iteratively applied to optimize the solution for the optical parameters. In this paper, we propose a novel neural network-based approach for solving this inverse problem, and validate its performance using experimentally measured diffuse reflectance data from a previously reported phantom study. Our inverse model was developed from a neural network forward model that was pre-trained with data from Monte Carlo simulations. The neural network forward model then creates a lookup table to invert the diffuse reflectance to the optical coefficients. We describe the construction of the neural network-based inverse model and test its ability to accurately retrieve optical properties from experimentally acquired diffuse reflectance data in liquid optical phantoms. Our results indicate that the developed neural network-based model achieves comparable accuracy to traditional Monte Carlo-based inverse model while offering improved speed and flexibility, potentially providing an alternative for developing faster clinical diagnosis tools. This study highlights the potential of neural networks in solving inverse problems in diffuse reflectance spectroscopy.