Year: 2025
Type: software-paper
Abstract: SignificanceAccurate 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.AimNIRFASTerFF is a cross-platform (Linux, macOS, and Windows) Python package for finite element method (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.ApproachNIRFASTerFF 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.ResultsCompared with its predecessor, NIRFASTer, the optimized algorithms provide a performance boost of 25% to 45% on GPU and up to 20% on CPU, and the results show good agreement with both Monte Carlo and analytical solutions.ConclusionThe 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.
Source: Journal of Biomedical Optics
Institutions: University of Birmingham, Washington University in St. Louis
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,675
OpenAlex ID: W4415787544