Haralick texture feature analysis for characterization of specific energy and absorbed dose distributions across cellular to patient length scales.

To investigate an approach for quantitative characterization of the spatial distribution of dosimetric data by introducing Haralick texture feature analysis in this context.Monte Carlo simulations are used to generate 3D arrays of dosimetric data for 2 scenarios: (1) cell-scale microdosimetry: specific energy (energy imparted per unit mass) in cell-scale targets irradiated by photon spectra (I,Ir, 6 MV); (2) tumour-scale dosimetry: absorbed dose in voxels for idealized models ofI permanent implant prostate brachytherapy, considering 'TG186' (realistic tissues including 0% to 5% intraprostatic calcifications; interseed attenuation) and 'TG43' (water model, no interseed attenuation) conditions. Five prominent Haralick features (homogeneity, contrast, correlation, local homogeneity, entropy) are computed and trends are interpreted using fundamental radiation physics.In the cell-scale scenario, the Haralick measures quantify differences in 3D specific energy distributions due to source spectra. For example, contrast and entropy are highest forI reflecting the large variations in specific energy in adjacent voxels (photoelectric interactions; relatively short range of electrons), while 6 MV has the highest homogeneity with smaller variations in specific energy between voxels (Compton scattering dominates; longer range of electrons). For the tumour-scale scenario, the Haralick measures quantify differences due to TG186/TG43 simulation conditions and the presence of calcifications. For example, as calcifications increase from 0% to 5%, contrast increases while correlation decreases, reflecting the large differences in absorbed dose in adjacent voxels (higher absorbed dose in voxels with calcification due to photoelectric interactions).Haralick texture analysis provides a quantitative method for the characterization of 3D dosimetric distributions across cellular to tumour length scales, with promising future applications including analyses of multiscale tissue models, patient-specific data, and comparison of treatment approaches.