ArticleRadiation oncology (London, England)2026
Dose calculation accuracy with extended CT scales near metal: phantom-patient evaluation using a beam-metal overlap metric.
Article in Radiation oncology (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundWe evaluated whether 16-bit CT reconstruction with explicit CT-to-electron-density (CT-to-ED) calibration improves dose-calculation accuracy versus conventional 12-bit reconstruction in the presence of metallic implants, and whether geometric metrics can identify cases that benefit most.
methodsCT-to-ED tables were built for both bit depths. In Solid Water, dose profiles and absolute dose were compared against EBT4 film and TG-51 ion-chamber measurements. Eight clinical IMRT plans were optimized and cross-recalculated between bit-depth domains with segments and monitor units fixed. A beam–metal interaction metric, ηmetal (monitor-unit–weighted beam’s-eye-view overlap), and the volumetric overlap ratio (VOR) between metal and PTV were computed. Gamma passing rate (GPR, 3 mm/3%), mean gamma, profile error, and absolute-dose deviation were evaluated.
resultsIn the phantom, 16-bit improved agreement over 12-bit, yielding higher GPR, profile differences ≤ 2%, and absolute-dose errors ≤ 1.0%; 12-bit showed profile errors up to 5.4% (stainless steel) and 3.0% (titanium) and absolute-dose errors of 2.3% and 1.7%. In patients, ηmetal > 35% was associated with GPR < 90% when 12-bit plans were recalculated on 16-bit, whereas low ηmetal maintained ≥ 90%. ηmetal correlated positively with VOR and negatively with GPR, supporting pre-treatment triage via VOR.
conclusionsExtended-bit reconstruction mitigates HU saturation, improves RED assignment, and yields better agreement with measurements and calculation. These findings support adopting 16-bit CT with CT-to-ED calibration as the default for planning and verification in patients with metallic implants, with VOR or ηmetal flagging cases where benefit is greatest.
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