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Updated: May 25, 2026

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
Published on: February 6, 2019
Dual-energy CT for proton therapy: Impact of advanced slice-wise patient-thickness estimation methods for improved
Julian Schwengfelder1,2, Nils Peters3, Patrick Wohlfahrt4
1OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine, Dresden, Saxony, Germany.
Background:
The direct prediction of stopping-power ratio (SPR) from dual-energy CT (DECT) has become gold-standard in proton therapy. Remaining uncertainties due to patient-size-specific CT number variations are mitigated by calibration factors based on patient size defined as water-equivalent thickness.
Purpose:
To improve SPR prediction, two slice-wise thickness estimation methods (TEM-B1 and -B2) were compared with the previously used one (TEM-A).
Methods:
TEM-A is using the maximum attenuation projections in x- and y-direction, while TEM-B1 and -B2 incorporate all voxels of the object to better describe non-elliptical geometries. Simplified geometries were used to investigate TEM dependencies on several parameters (e.g., object shape, rotation). TEMs were then applied to DECT scans of cylindrical acrylic phantoms with varying diameters and to patient data. Clinical treatment plans were recalculated on generated TEM-specific SPR datasets, and the impact of different estimated thicknesses on SPR was assessed.
Results:
In contrast to TEM-A, TEM-B1 and -B2 demonstrated robustness to object shape and rotation. Couch attenuation affected all evaluated TEMs with TEM-A being most affected. For patient scans, TEM-B1 and B2 agreed closely but differed from TEM-A, especially in high diameters. In obese patients, this leads to relative proton range deviations up to 0.3% when comparing TEM-B1 and TEM-A. In the sensitivity analysis, TEM-B1 and -B2 maintained SPR uncertainties below ±3% even for cortical bone.
Conclusions:
TEM-B1 and -B2 reduced deviations in thickness estimation and increased robustness to object shape, overcoming the limitations of TEM-A and improving SPR prediction accuracy.
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