The purpose of this study was to analyze factors affecting proton stopping-power-ratio (SPR) estimations and range uncertainties in proton therapy planning using the standard stoichiometric calibration. The SPR uncertainties were grouped into five categories according to their origins and then estimated based on previously published reports or measurements. For the first time, the impact of tissue composition variations on SPR estimation was assessed and the uncertainty estimates of each category were determined for low-density (lung), soft, and high-density (bone) tissues. A composite, 95th percentile water-equivalent-thickness uncertainty was calculated from multiple beam directions in 15 patients with various types of cancer undergoing proton therapy. The SPR uncertainties (1σ) were quite different (ranging from 1.6% to 5.0%) in different tissue groups, although the final combined uncertainty (95th percentile) for different treatment sites was fairly consistent at 3.0–3.4%, primarily because soft tissue is the dominant tissue type in human body. The dominant contributing factor for uncertainties in soft tissues was the degeneracy of Hounsfield Numbers in the presence of tissue composition variations. To reduce the overall uncertainties in SPR estimation, the use of dual-energy computed tomography is suggested. The values recommended in this study based on typical treatment sites and a small group of patients roughly agree with the commonly referenced value (3.5%) used for margin design. By using tissue-specific range uncertainties, one could estimate the beam-specific range margin by accounting for different types and amounts of tissues along a beam, which may allow for customization of range uncertainty for each beam direction.

We discovered an empirical relationship between the logarithm of mean excitation energy (ln Im) and the effective atomic number (EAN) of human tissues, which allows for computing patient-specific proton stopping power ratios (SPRs) using dual-energy CT (DECT) imaging. The accuracy of the DECT method was evaluated for 'standard' human tissues as well as their variance. The DECT method was compared to the existing standard clinical practice-a procedure introduced by Schneider et al at the Paul Scherrer Institute (the stoichiometric calibration method). In this simulation study, SPRs were derived from calculated CT numbers of known material compositions, rather than from measurement. For standard human tissues, both methods achieved good accuracy with the root-mean-square (RMS) error well below 1%. For human tissues with small perturbations from standard human tissue compositions, the DECT method was shown to be less sensitive than the stoichiometric calibration method. The RMS error remained below 1% for most cases using the DECT method, which implies that the DECT method might be more suitable for measuring patient-specific tissue compositions to improve the accuracy of treatment planning for charged particle therapy. In this study, the effects of CT imaging artifacts due to the beam hardening effect, scatter, noise, patient movement, etc were not analyzed. The true potential of the DECT method achieved in theoretical conditions may not be fully achievable in clinical settings. Further research and development may be needed to take advantage of the DECT method to characterize individual human tissues.

The conventional kilovoltage (kV) x-ray-based dual-energy CT (DECT) imaging using two different x-ray energy spectra is sensitive to image noise and beam hardening effects. The purpose of this study was to evaluate the theoretical advantage of the DECT method for determining proton stopping power ratios (SPRs) using a combination of kV and megavoltage (MV) x-ray energies. We investigated three representative x-ray energy pairs: 100-kVp and 140-kVp comprised the kV-kV pair, 100-kVp and 1-MV comprised the kV-MV pair, and two 1-MV x-ray beams – one with and one without external filtration comprised the MV-MV pair. The SPRs of 34 human tissues were determined using the DECT method with these three x-ray energy pairs. Small perturbations were introduced into the CT numbers and x-ray spectra used for the DECT calculation to simulate the effects of random noise and beam hardening. An error propagation analysis was performed on the DECT calculation algorithm to investigate the propagation of CT number uncertainty to final SPR estimation and to suggest the best x-ray energy combination. We found that the DECT method using each of the three beam pairs achieved similar accuracy in determining the SPRs of human tissues in ideal conditions. However, when CT number uncertainties and artifacts such as imaging noise and beam hardening effects were considered, the kV-MV DECT improved the accuracy of SPR estimation substantially over the kV-kV or MV-MV DECT methods. Furthermore, our error propagation analysis showed that the combination of 100-kVp and 1-MV beams was close to the optimal selection when using the DECT method to determine SPRs. Overall, the kV-MV combination makes the DECT method more robust in resolving the effective atomic numbers for biological tissues than the traditional kV-kV DECT method.

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