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Accurate breast cancer screening requires precise mammography dose estimation. This study introduces a patient-specific method to calculate dose ranges, accounting for tissue variations, improving early detection and patient safety.

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Area of Science:

  • Medical Physics
  • Radiology
  • Oncology

Background:

  • Breast cancer screening mammography is crucial for early detection and mortality reduction.
  • Current dose estimation methods use simplified breast models, neglecting tissue heterogeneity.
  • This can lead to inaccurate normalized glandular dose (DgN) estimates.

Purpose of the Study:

  • To investigate the impact of fibroglandular tissue distribution on mammographic dose.
  • To propose a patient-specific framework for estimating normalized glandular dose (DgN) range.
  • To enable more personalized and transparent mammographic dose assessment.

Main Methods:

  • Monte Carlo simulations to assess DgN variations based on fibroglandular tissue placement.
  • Development of a patient-specific framework using mammographic projections and glandular fraction (GF) maps.
  • Siddon ray-tracing back projection for volumetric reconstruction and dose calculation.

Main Results:

  • Fibroglandular tissue distribution significantly impacts DgN, with variations up to a factor of three.
  • Patient-specific framework successfully estimated DgN ranges from single mammographic projections.
  • Reconstructed volumes showed dose minimization/maximization based on tissue placement.

Conclusions:

  • Realistic bounds on patient dose can be derived from limited mammographic imaging data.
  • The proposed framework supports personalized mammographic dose assessment.
  • Accounting for tissue heterogeneity enhances the accuracy and transparency of screening mammography.