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

Area of Science:

• Medical Physics
• Radiotherapy
• Image Reconstruction

Background:

• Photon-counting CT (PCCT) offers reduced noise and spectral separation for improved tissue stopping power (SPR) calculation.
• Current validation of PCCT benefits often uses simplified phantoms, not reflecting complex patient anatomy.

Purpose of the Study:

• To propose virtual imaging simulation as an alternative for validating beam range uncertainty in complex patient geometries.
• To validate SPR calculation accuracy using a computational head phantom and PCCT system model.
• To compare a prototype software (TissueXplorer) against conventional methods for SPR estimation.

Main Methods:

• Utilized a validated CT simulator (DukeSim) to generate PCCT projections of a computational head phantom.
• Reconstructed projections using the ASTRA toolbox and simulated a 2 Gy proton dose for nasal and brain tumors.
• Estimated SPR values using conventional stoichiometric calibration and TissueXplorer, comparing dose distributions against a ground-truth plan.

Main Results:

• TissueXplorer achieved a mean percentage difference of 0.28% in SPR estimation across head tissues.
• SPRs calculated with TissueXplorer resulted in smaller dose distribution differences compared to the conventional method.
• Virtual imaging demonstrated the potential for more accurate SPR prediction in complex anatomical models.

Conclusions:

• Virtual imaging provides a viable method for validating SPR prediction and its impact on dose distribution in radiotherapy.
• Software utilizing spectral information, like TissueXplorer, shows promise for superior SPR prediction accuracy over conventional approaches.
• This simulation study supports the use of advanced imaging techniques for enhancing radiotherapy treatment planning and outcomes.