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

Area of Science:

• Medical Physics
• Computational Imaging
• Radiotherapy Physics

Background:

• Accurate beam range prediction is crucial for effective proton therapy.
• Uncertainty in stopping power ratio (SPR) calculations can impact dose distribution.
• Experimental validation can be complex, especially in intricate patient geometries.

Purpose of the Study:

• To propose and validate virtual imaging simulators as an alternative to experimental methods for assessing beam range uncertainty.
• To compare the accuracy of SPR calculations using a prototype software (TissueXplorer) against a conventional approach.
• To evaluate the impact of SPR prediction accuracy on dose distribution in a computational head model.

Main Methods:

• Utilized a validated CT simulator (DukeSim) to generate photon-counting CT projections of a computational head model.
• Reconstructed CT images using the ASTRA toolbox.
• Calculated SPR values using both conventional stoichiometric calibration and TissueXplorer software.
• Performed proton therapy dose calculations for nasal and brain tumors on the computational model.

Main Results:

• TissueXplorer achieved a mean percentage difference of 0.28% in SPR estimation across head tissues.
• SPR values derived from TissueXplorer resulted in smaller dose distribution differences compared to the ground truth plan than the conventional method.
• Virtual imaging simulations provided a viable alternative for validating SPR predictions and their dosimetric impact.

Conclusions:

• Virtual imaging simulators can serve as an effective tool for validating beam range uncertainty in proton therapy.
• TissueXplorer, leveraging spectral information, demonstrates superior accuracy in SPR prediction over conventional methods.
• Accurate SPR prediction is essential for optimizing dose distribution and improving clinical outcomes in proton therapy.