Nominal material density may not reflect TPS-relevant density for 3D-printed surgical reconstruction implants: An educational case study.

Authors

Eric D Ehler, Shane Edlund, Margaret Reynolds

Published in

Journal of applied clinical medical physics. Volume 27. Issue 8. Pages e70716.

Abstract

Additively manufactured (3D-printed) surgical reconstruction implants are increasingly encountered in head and neck oncology and may introduce density assignment challenges during radiotherapy treatment planning. This case report describes a clinically significant dosimetric discrepancy arising from the application of nominal bulk material density to an additively manufactured surgical reconstruction implant and to highlight workflow considerations for density assignment. A patient treated with helical tomotherapy following mandibular reconstruction with an additively manufactured titanium implant underwent kilovoltage computed tomography (kVCT) simulation. Saturation of Hounsfield units within the implant precluded reliable density estimation, necessitating a density override based on nominal bulk material properties. During treatment, megavoltage computed tomography (MVCT) imaging with adaptive dose recalculation was used to evaluate the dosimetric impact of this assumption. Replanning was performed using a revised density override informed by imaging-based evaluation while maintaining institutional planning criteria. Adaptive dose recalculation demonstrated a localized calculated dose increase of approximately 7% within portions of the clinical and planning target volumes adjacent to the reconstruction implant. Replanning with a revised density override reduced unintended target dose heterogeneity while preserving target coverage. Nominal bulk material density may not accurately represent TPS-relevant radiologic density for additively manufactured surgical reconstruction implants, and this discrepancy may not be apparent on kVCT imaging. Independent evaluation of density assumptions using alternative imaging modalities can identify clinically meaningful discrepancies and support patient-protective replanning decisions.

PMID:
42503597
Bibliographic data and abstract were imported from PubMed on 27 Jul 2026.

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