Stylized versus voxel phantoms: quantification of internal organ chord length distances

Le résumé fourni par la source

Dosimetric calculations, whether for radiation protection or nuclear medicine applications, are greatly influenced by the use of computational models of humans, called anthropomorphic phantoms. As anatomical models of phantoms have evolved and expanded, thus has the need for quantifying differences among each of these representations that yield variations in organ dose coefficients, whether from external radiation sources or internal emitters. This work represents an extension of previous efforts to quantify the differences in organ positioning within the body between a stylized and voxel phantom series. Where prior work focused on the organ depth distribution vis-à-vis the surface of the phantom models, the work described here quantifies the intra-organ and inter-organ distributions through calculation of the mean chord lengths. The revised Oak Ridge National Laboratory stylized phantom series and the University of Florida/National Cancer Institute voxel phantom series including a newborn, 1-, 5-, 10- and 15 year old, and adult phantoms were compared. Organ distances in the stylized phantoms were computed using a ray-tracing technique available through Monte Carlo radiation transport simulations in MCNP6. Organ distances in the voxel phantom were found using phantom matrix manipulation. Quantification of differences in organ chord lengths between the phantom series displayed that the organs of the stylized phantom series are typically situated farther away from one another than within the voxel phantom series. The impact of this work was to characterize the intra-organ and inter-organ distributions to explain the variations in updated internal dose coefficient quantities (i.e. specific absorbed fractions) while providing relevant data defining the spatial and volumetric organ distributions in the phantoms for use in subsequent internal dosimetric computations, with prospective relevance to patient-specific individualized dosimetry, as well as informing machine learning definition of organs using these reference models.

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