ArticleMedical physics2026
Theoretical assessment of DECT noise on physical and biological dose accuracy in carbon-ion radiation therapy.
Article in Medical physics, 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
backgroundAccurate characterization of tissue parameters is essential for precise dose calculation in Carbon Ion Radiation Therapy (CIRT). Recent advances in Dual-Energy CT (DECT) have improved the estimation of tissue parameters, yet DECT-based methods are susceptible to image noise. The influence on physical and biological dose accuracy has not been thoroughly investigated, undermining the evidence-based clinical application and protocol optimization.
objectiveTo systematically examine how image noise in DECT influences the estimated tissue parameters and its consequent impact on the accuracy of physical and biological dose distributions in CIRT. MATERIALS AND
methodsFour DECT-based elemental decomposition methods were evaluated. A machine-learning (ML) approach was compared with three parameterization (PA) methods, that is, the Hünemohr model using input parameters (
resultsAcross the investigated noise levels, the ML approach consistently outperformed the three PA methods. Compared with PA-based methods, the ML approach reduced the average water-equivalent range deviations by 0.4 mm-1.4 mm and improved gamma passing rates by 0.8%-3.9% (physical dose) and 6.4%-24.1% (biological dose) under the 1 mm/1% criteria.
conclusionThe ML method provides superior robustness and accuracy in physical and biological dose calculation based on DECT of various noise levels. The LEM demonstrated superior noise robustness compared to the LQM and the MKM.
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