2026-10-07T22:26:20Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34027152 2026-09-29T17:57:14Z category_30130 category_30127 category_30136 category_29779 category_29821 item_type_3 month_year_09_2026

<p dir="ltr"><b>Dataset and Source Code for "Boundary-Resolved Multispectral Optical Heterogeneity Reconstruction in Tumor-Like Media: A Multiregion Boundary Element Methodology with Spectral Inversion and Sensitivity Analysis"</b></p> Mohamed Abdelsabour Fahmy (10769228) Photonics, optoelectronics and optical communications Nonlinear optics and spectroscopy Atomic, molecular and optical physics not elsewhere classified Applied mathematics not elsewhere classified Numerical and computational mathematics not elsewhere classified boundary element method diffuse optical tomography multispectral imaging tumor heterogeneity inverse problems photon migration sensitivity analysis computational biophotonics <p dir="ltr">This repository accompanies the manuscript <b>“Boundary-Resolved Multispectral Optical Heterogeneity Reconstruction in Tumor-Like Media: A Multiregion Boundary Element Methodology with Spectral Inversion and Sensitivity Analysis.”</b></p><p dir="ltr">The study develops a computational biophotonics framework for reconstructing wavelength-dependent optical heterogeneity in piecewise-homogeneous tumor-like media using the <b>Boundary Element Method (BEM)</b>. The model treats optically distinct tissue compartments as explicit subdomains separated by internal interfaces and solves photon migration using the diffusion approximation with continuity of fluence and conservation of diffusive flux across those interfaces. The methodology combines multispectral forward modeling, constrained regional absorption reconstruction, sensitivity analysis based on the derivative of the assembled BEM system, and quantitative heterogeneity measures including the <b>Optical Heterogeneity Index (OHI)</b> and <b>Boundary Optical Heterogeneity Index (BOHI)</b>.</p><p dir="ltr">The numerical study considers a two-dimensional circular tissue domain containing two tumor-like subregions and evaluates the framework at <b>660, 780, and 850 nm</b>. Verification includes matched-model consistency tests, boundary-mesh refinement, Gaussian-quadrature refinement, finite-difference verification of the sensitivity operator, held-out detector prediction, and comparison with an independently implemented finite-element forward model. The reported matched-model reconstruction recovers the prescribed regional absorption spectra to numerical precision, while the independent FEM/BEM comparison reveals non-negligible forward-model discrepancy, emphasizing the distinction between code consistency and independent validation.</p><p dir="ltr">This Figshare deposit provides a <b>repository-ready reproducibility package</b> containing machine-readable numerical data, benchmark configuration files, reference Python modules for geometry, optical kernels and numerical metrics, scripts for reproducing reported figures and tables, verification datasets, unit tests, documentation, software-environment files, citation metadata, and checksum information.</p><p dir="ltr">The package is intended to support transparent numerical verification, reuse, and extension of the proposed methodology in <b>diffuse optical tomography, computational biophotonics, tissue optics, inverse problems, and interface-resolved optical modeling</b>. It should be interpreted as a computational research resource rather than a clinically validated diagnostic or biopsy-replacement system.</p> 2026-09-29T17:57:14Z Dataset Dataset 10.6084/m9.figshare.34027152.v1 https://figshare.com/articles/dataset/_p_dir_ltr_b_Dataset_and_Source_Code_for_Boundary-Resolved_Multispectral_Optical_Heterogeneity_Reconstruction_in_Tumor-Like_Media_A_Multiregion_Boundary_Element_Methodology_with_Spectral_Inversion_and_Sensitivity_Analysis_b_p_/34027152 CC BY 4.0