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NIRFASTerFF: an accessible, cross-platform Python package for fast photon modeling
Jiaming Cao1, Samuel Montero-Hernandez1, Rickson C Mesquita1
1University of Birmingham, School of Computer Science, Birmingham, United Kingdom.
Journal of Biomedical Optics
|November 7, 2025
Summary
NIRFASTerFF is a new Python package for efficient photon modeling in diffuse optical imaging. This tool accelerates computations on CPUs and GPUs, benefiting biophotonics research.
Area of Science:
- Biomedical Optics
- Computational Imaging
- Photonics
Background:
- Accurate photon modeling is crucial for diffuse optical imaging (DOI).
- Model-based analysis and image reconstruction offer significant educational and research advantages in DOI.
Purpose of the Study:
- Introduce NIRFASTerFF, a cross-platform Python package for finite element method (FEM)-based light propagation modeling.
- Support continuous-wave, frequency-domain, and time-resolved data for optical imaging, including autocorrelation function modeling for diffuse correlation spectroscopy.
- Validate NIRFASTerFF against existing tools like NIRFAST and Monte Carlo simulations.
Main Methods:
- Utilize highly parallelized FEM solvers with OpenMP and CUDA for CPU and GPU acceleration.
- Implement voxel-based interpolation of optical fluence for image reconstruction tasks.
- Leverage Python for a cross-platform (Linux, macOS, Windows) solution.
Main Results:
- Achieve a performance increase of 25%-45% on GPU and up to 20% on CPU compared to NIRFAST.
- Demonstrate good agreement between NIRFASTerFF results and both Monte Carlo and analytical solutions.
- Provide a flexible and accurate forward solution for inverse problem formulations.
Conclusions:
- NIRFASTerFF offers a fast, license-free tool for photon modeling in diffuse optical imaging.
- Streamline Python-based data processing within the biophotonics community.
- Enhance the capabilities of model-based analysis and image reconstruction in optical imaging research.
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