Development and Validation of Patient-Specific Monte Carlo Dosimetry for Synchrotron Breast Phase-Contrast CT
Amir Entezam, Ashkan Pakzad, Christopher J. Hall, Anton Maksimenko, Matthew John Cameron, Adam Round, Mojtaba Hoseini-Ghahfarokhi, Seyedamir T. Taba, Yakov I. Nesterets, Daniel Hausermann, Magdalena Bazalova-Carter, Patrick C. Brennan, Timur E. Gureyev, Harry M. Quiney

TL;DR
This paper presents a patient-specific Monte Carlo dosimetry framework for synchrotron breast phase-contrast CT, enabling accurate mean glandular dose estimation considering individual anatomy.
Contribution
It introduces a unified, voxel-based Monte Carlo framework using EGSnrc for realistic patient-specific dosimetry in synchrotron breast CT.
Findings
MGD varies significantly with anatomy and energy levels.
Higher glandular density reduces MGD, larger breast volume increases dose.
A 2 mm increase in skin thickness reduces MGD by 10%.
Abstract
This study develops and validates a patient-specific Monte Carlo (MC) dosimetry framework for propagation-based phase-contrast breast CT (BCT) at the Imaging and Medical Beamline (IMBL), ANSTO Australian Synchrotron, for accurate mean glandular dose (MGD) estimation. BCT provides 3D imaging without breast compression, improving comfort and visualization of internal structures for cancer detection. Propagation-based phase contrast improves soft-tissue contrast at equal or lower dose than conventional systems. Accurate dosimetry remains essential for safety and optimisation. Most MC-based MGD studies use non-patient-specific phantoms that ignore anatomical variability, while existing patient-specific methods lack a unified framework. Here, a voxel-based MC framework using EGSnrc was implemented to compute MGD in realistic anthropomorphic breast phantoms derived from synchrotron BCT…
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