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Proton therapy range uncertainty reduction using vendor-agnostic tissue characterization on a virtual photon-counting CT head scan

S. Vrbaški, G. Stanić, S. Molinelli, M. Bhattarai, E. Abadi, M. Ciocca, E. Samei

TL;DR

This paper tackles proton therapy range uncertainty arising from CT-to-SPR conversions. It introduces a virtual imaging trial framework using a validated CT simulator (DukeSim) and anthropomorphic head phantoms to quantify SPR accuracy and its impact on dose, comparing conventional stoichiometric calibration with TissueXplorer, a vendor-agnostic spectral-tissue tool. The study finds a mean voxel-wise SPR difference of $0.28\%$ with TissueXplorer and shows dose distributions that more closely match ground truth than the SE-PCCT calibration method. The results support spectral CT-based SPR estimation as a means to reduce range uncertainty and potentially tighten treatment margins, while recognizing limitations such as single-case evaluation and the need for broader validation across multiple beams and patient variability.

Abstract

In this work, we proposed virtual imaging simulators as an alternative approach to experimental validation of beam range uncertainty in complex patient geometry using a computational model of a human head and a photon-counting CT scanner. We validate the accuracy of stopping power ratio (SPR) calculations using a conventional stoichiometric calibration approach and a prototype software, TissueXplorer. A validated CT simulator (DukeSim) was used to generate photon-counting CT projections of a computational head model, which were reconstructed with an open-source toolbox (ASTRA). The dose of 2 Gy was delivered through protons in a single fraction to target two different cases of nasal and brain tumors with a single lateral beam angle. Ground truth treatment plan was made directly on the computational head model using clinical treatment planning software (RayStation). This plan was then recalculated on the corresponding CT images for which SPR values were estimated using both the conventional method and the prototype software TissueXplorer. The mean percentage difference in estimating the stopping power ratio with TissueXplorer in all head tissues inside the scanned volume was 0.28%. Stopping power ratios obtained with this method showed smaller dose distribution differences from the ground truth plan than the conventional stoichiometric calibration method on the computational head model. Virtual imaging offers an alternative approach to validation of the SPR prediction from CT imaging, as well as its effect on the dose distribution and thus downstream clinical outcomes. According to this simulation study, software solutions that utilize spectral information, such as TissueXplorer, hold promise for more accurate prediction of the stopping power ratio than the conventional stoichiometric approach.

Proton therapy range uncertainty reduction using vendor-agnostic tissue characterization on a virtual photon-counting CT head scan

TL;DR

This paper tackles proton therapy range uncertainty arising from CT-to-SPR conversions. It introduces a virtual imaging trial framework using a validated CT simulator (DukeSim) and anthropomorphic head phantoms to quantify SPR accuracy and its impact on dose, comparing conventional stoichiometric calibration with TissueXplorer, a vendor-agnostic spectral-tissue tool. The study finds a mean voxel-wise SPR difference of with TissueXplorer and shows dose distributions that more closely match ground truth than the SE-PCCT calibration method. The results support spectral CT-based SPR estimation as a means to reduce range uncertainty and potentially tighten treatment margins, while recognizing limitations such as single-case evaluation and the need for broader validation across multiple beams and patient variability.

Abstract

In this work, we proposed virtual imaging simulators as an alternative approach to experimental validation of beam range uncertainty in complex patient geometry using a computational model of a human head and a photon-counting CT scanner. We validate the accuracy of stopping power ratio (SPR) calculations using a conventional stoichiometric calibration approach and a prototype software, TissueXplorer. A validated CT simulator (DukeSim) was used to generate photon-counting CT projections of a computational head model, which were reconstructed with an open-source toolbox (ASTRA). The dose of 2 Gy was delivered through protons in a single fraction to target two different cases of nasal and brain tumors with a single lateral beam angle. Ground truth treatment plan was made directly on the computational head model using clinical treatment planning software (RayStation). This plan was then recalculated on the corresponding CT images for which SPR values were estimated using both the conventional method and the prototype software TissueXplorer. The mean percentage difference in estimating the stopping power ratio with TissueXplorer in all head tissues inside the scanned volume was 0.28%. Stopping power ratios obtained with this method showed smaller dose distribution differences from the ground truth plan than the conventional stoichiometric calibration method on the computational head model. Virtual imaging offers an alternative approach to validation of the SPR prediction from CT imaging, as well as its effect on the dose distribution and thus downstream clinical outcomes. According to this simulation study, software solutions that utilize spectral information, such as TissueXplorer, hold promise for more accurate prediction of the stopping power ratio than the conventional stoichiometric approach.
Paper Structure (9 sections, 4 equations, 5 figures, 2 tables)

This paper contains 9 sections, 4 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Virtual imaging framework
  4. Ground truth SPR map
  5. TissueXplorer SPR
  6. Treatment plan setup
  7. Results
  8. Discussion
  9. Conclusion

Figures (5)

  • Figure 1: Computational (virtual) binary phantoms used for (a) generating a calibration curve and (b) treatment planning with placeholders (marked with different colors) for materials of different composition.
  • Figure 2: Linear HU-mass density calibration curve (120 kVp, 20 keV threshold) based on CIRS phantom inserts used for commissioning of the single energy photon counting CT.
  • Figure 3: Dose distribution in XCAT head phantom ground-truth plan obtained using known material compositions in table \ref{['tab:composition']} and equation \ref{['eq:spr']} for (a) nasal tumor CTV and (d) brain tumor CTV; recalculated SE-PCCT plan obtained using stochiometric calibration curve in figure \ref{['fig:calibration']} for (b) nasal tumor CTV and (e) brain tumor CTV; and recalculated PCCT plan obtained with TissueXplorer software as described in section \ref{['sec:tissueXplorer']} for (c) nasal tumor CTV and (f) brain tumor CTV. The total dose is 2 Gy (RBE) single-fraction, and the colorbar unit is in Gy.
  • Figure 4: Difference in dose distribution between ground-truth plan and recalculated single-energy photon-counting CT (SE-PCCT) plan for (a) nasal tumor CTV and (c) brain tumor CTV, and ground-truth plan and recalculated PCCT plan for (b) nasal tumor CTV and (d) brain tumor CTV. The scale bar shows percent differences.
  • Figure 5: Dose-volume histograms extracted from the ground-truth plan (full lines), single-energy photon-counting CT (SE-PCCT) plan with SPR obtained through the stoichiometric calibration curve in Figure \ref{['fig:calibration']} (dashed lines), and the spectral PCCT plan with SPR obtained through the commercial prototype TissueXplorer (dotted lines). Dose-volume curves for nasal tumor (a) are given for the left eye (blue), right eye (orange), left optical nerve (green), right optical nerve (red), optic chiasm (purple), outer body (brown), and the CTV in the nasal cavity (pink). Dose-volume curves for brain tumor (b) are given for the optic chiasm (red), outer body (blue), brainstem (orange), and the CTV in the proximity of the brainstem (green).