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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
algorithmdiffuse opticsfinite element methodmathematical modelingparallel computingtoolbox

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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.