NIRFASTerFF: an accessible, cross-platform Python package for fast photon modeling

Significance: Accurate and efficient photon modeling plays an essential role in the rapidly developing field of diffuse optical imaging, whereby the use of model-based analysis and image reconstruction can provide both educational and research benefits.

Aim: NIRFASTerFF is a cross-platform (Linux, macOS, and Windows) Python package for FEM-based light propagation modeling, supporting continuous-wave, frequency-domain, and time-resolved data for both exogenous and endogenous optical imaging applications. It also enables modeling of the autocorrelation function (G₁) for diffuse correlation spectroscopy. Validation is performed through comparison with the original NIRFAST and gold-standard Monte Carlo simulations.

Approach: NIRFASTerFF incorporates highly parallelized FEM solvers for efficient computation on both CPU and GPU, leveraging OpenMP and CUDA acceleration. To support image reconstruction tasks, voxel-based interpolation of the optical fluence is implemented, providing a flexible and accurate representation of the forward solution suitable for inverse problem formulations.

Results: Compared to its predecessor, NIRFASTer, the optimized algorithms provide a performance boost of 25–45% on GPU and up to 20% on CPU, and the results show good agreement with both Monte Carlo and analytical solutions.

Conclusions: The NIRFASTerFF package provides a fast and license-free tool for photon modeling and can further streamline Python-based data processing in diffuse optical imaging, benefiting the biophotonics community.