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Statistical shape modeling as a practical tool for dosimetry: quantifying uncertainty in internal dose assessment.

Mahsa Noorvand1, Farshid Babapour Mofrad1, Elham Saeedzadeh1

  • 1Department of Medical Radiation Engineering, SR.C., Islamic Azad University, Tehran, Iran.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|April 24, 2026
PubMed
Summary

Statistical Shape Modeling (SSM) creates realistic computational phantoms reflecting anatomical variability. This improves internal dosimetry accuracy by showing significant differences in Specific Absorbed Fraction (SAF) calculations compared to average models.

Keywords:
Internal dosimetryMonte carlo simulationPopulation based phantomSpecific absorbed fractions (SAFs)Statistical shape modelUncertainty

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Area of Science:

  • Medical Physics
  • Computational Biology
  • Radiological Sciences

Background:

  • Current internal dosimetry models use average anatomical data, limiting accuracy.
  • Inter-individual variations in organ size and shape are not adequately represented.
  • There is a need for more realistic computational phantoms in dosimetry.

Purpose of the Study:

  • Introduce Statistical Shape Modeling (SSM) for creating population-based computational phantoms.
  • Investigate the impact of anatomical variability on Specific Absorbed Fraction (SAF) calculations.
  • Enhance the accuracy and reliability of internal dose assessments.

Main Methods:

  • Developed population-based computational phantoms using SSM integrated with a template phantom.
  • Incorporated organ models reflecting natural anatomical variability.
  • Computed SAF values using Monte Carlo simulations (GATE code) with variable lung-sized phantoms.

Main Results:

  • Significant discrepancies in SAF values were found between population-based and average phantoms.
  • Percentage differences in SAF values ranged from 19.96% to 54.71% at 0.01 MeV photon energy.
  • Demonstrated the critical influence of organ size variability on SAF calculations.

Conclusions:

  • SSM is a practical and effective tool for generating population-based computational phantoms.
  • The methodology improves the accuracy of SAF calculations and internal dosimetry.
  • Recommends extending SSM to other organs for personalized dosimetry and risk assessment.