Studies have shown large variations in stopping-power ratio (SPR) prediction from computed tomography (CT) across European proton centres. To standardise this process, a step-by-step guide on specifying a Hounsfield look-up table (HLUT) is presented here.
The HLUT specification process is divided into six steps: Phantom setup, CT acquisition, CT number extraction, SPR determination, HLUT specification, and HLUT validation. Appropriate CT phantoms have a head- and body-sized part, with tissue-equivalent inserts in regard to X-ray and proton interactions. CT numbers are extracted from a region-of-interest covering the inner 70% of each insert in-plane and several axial CT slices in scan direction. For optimal HLUT specification, the SPR of phantom inserts is measured in a proton beam and the SPR of tabulated human tissues is computed stoichiometrically at 100 MeV. Including both phantom inserts and tabulated human tissues increases HLUT stability. Piecewise linear regressions are performed between CT numbers and SPRs for four tissue groups (lung, adipose, soft tissue, and bone) and then connected with straight lines. Finally, a thorough but simple validation is performed.
The best practices and individual challenges are explained comprehensively for each step. A well-defined strategy for specifying the connection points between the individual line segments of the HLUT is presented. The guide was tested exemplarily on three CT scanners from different vendors, proving its feasibility.
The presented step-by-step guide for CT-based HLUT specification with recommendations and examples can contribute to reduce inter-centre variations in SPR prediction.

The photon counting detector (PCD) in computed tomography (CT) can count the number of incoming photons in order to obtain energy information for photons corresponding to user-defined thresholds. Research on the extraction of effective atomic number (EAN) and relative electron density (RED) using dual-energy CT (DECT) is currently underway. This study proposes a method for improving EAN and RED accuracy of tissue-equivalent materials by using PCD-CT-based stoichiometric calibration. After obtaining DECT images in energy bin (EB) and full spectrum (FS) modes for eight tissue-equivalent materials, the EAN was calculated with stoichiometric calibration. Using the EAN image, the RED image was acquired to evaluate the accuracy. The errors of both EAN and RED obtained with EB were within 4%. In particular, the accuracy of RED was higher than that of the FS method. Study results indicate that PCD-CT contributes to improving EAN and RED accuracy. Further studies will be aimed at reducing ring artifacts by pixel-correcting PCD images and improving stopping power ratio (SPR) measurements for dose calculation in particle therapy.

OBJECTIVE: The purpose of this study was to quantify the variability of stoichiometric calibration curves for different computed tomography (CT) scanners and determine whether an averaged Hounsfield unit (HU)-to-stopping power ratio (SPR) calibration curve can be used across multiple CT scanners.
METHODS: Five CT scanners were used to scan an electron density phantom to establish HU values of known material plugs. A stoichiometric calibration curve was calculated for CT scanners and for the average curve. Animal tissue surrogates were used to compare the water-equivalent thickness (WET) of the animal tissue surrogates calculated by the treatment planning system (TPS) and the WET values measured with a multilayered ionization chamber. The calibration curves were optimized to reduce the percentage of difference between measured and TPS-calculated WET values. A second set of tissue surrogates was then used to evaluate the overall range of uncertainty for the optimized CT-specific and average calibration curves.
RESULTS: Overall, the average variation in HU for all 6 calibration curves before optimization was 8.3 HU. For both the averaged and CT-specific calibrations, the root mean square error (RMSE) of the percentage of difference between TPS-calculated and measured WET values before optimization was 4%. The RMSE of the percentage of difference for the TPS-calculated and multilayered ionization chamber measured WET values after the optimization for both averaged and CT-specific calibration curves was reduced to less than 1.5%. The overall RMSE of the TPS and the measured WET percentage of difference after optimization was 2.1% for both averaged and CT-specific calibration curves.
CONCLUSIONS: Averaged CT calibration curves can be used to map the HU-to-SPR in TPSs, if the variations in HU values across all scanners is relatively small. Performing tissue surrogate optimization of the HU-to-SPR calibration curve has been shown to reduce the overall uncertainty of the calibration for averaged and CT-specific calibration curves and is recommended, especially if an averaged HU-to-SPR calibration curve is used.

For CT-based dose calculation in ion therapy a link between the attenuation coefficients of photons and the stopping-power of particles has to be provided. There are two commonly known approaches to establish such a calibration curve, the stoichiometric calibration and direct measurements with tissue substitutes or animal samples. Both methods were investigated and compared. As input for the stoichiometric calibration the data from ICRP-report 23 were compared to newly available data from ICRP-report 110. By employing the newer data no relevant difference could be observed. The differences between the two acquisition methods (direct measurement and stoichiometric calibration) were systematically analyzed and quantified. The most relevant change was caused by the exchange of carbon and oxygen content in the substitutes in comparison to the data of the ICRP-reports and results in a general overshoot of the Bragg peak. The consequence of the differences between the calibration curves was investigated with treatment planning studies and iso-range surfaces. Range differences up to 6mm in treatment plans of the head were observed. Additionally two improvements are suggested which increase the accuracy of the calibration curve.