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

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Title: NIRFASTerFF: an accessible, cross-platform Python package for fast photon modeling.
Authors: Jiaming Cao, Montero-Hernandez, Samuel, Mesquita, Rickson C., Eggebrecht, Adam T., Dehghani, Hamid
Source: Journal of Biomedical Optics; Nov2025, Vol. 30 Issue 11, p1-26, 26p
Subject Terms: PHOTON transport theory, LIGHT propagation, IMAGE reconstruction algorithms, PYTHON programming language, ELECTRONIC data processing, IMAGE reconstruction, PARALLEL processing, FINITE element method
Abstract: 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 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 (G 1) 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 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract: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 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 (G 1) 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 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]
ISSN:10833668
DOI:10.1117/1.JBO.30.11.115001