Range prediction for tissue mixtures based on dual-energy CT

Area of Science:

• Medical Physics
• Radiotherapy Physics
• Computational Imaging

Background:

• Proton and ion therapy require precise range prediction for effective treatment planning.
• Dual-energy CT (DECT) offers potential for improved accuracy in determining physical target quantities.
• Accurate modeling of tissue mixtures and heterogeneities is crucial for clinical DECT application in radiotherapy.

Purpose of the Study:

• To modify existing DECT-based ion-range prediction methods to incorporate tissue mixing behavior.
• To improve the accuracy of ion range prediction in the presence of complex tissue heterogeneities.
• To validate a novel approach for handling mixed tissues in DECT for radiotherapy.

Main Methods:

• Factorization of the stopping-power ratio into relative electron density and relative stopping number.
• Relating the relative stopping number to the relative cross section obtained from DECT.
• Developing a method to handle mixing and averaging properties for various structural levels of tissue mixtures.

Main Results:

• Demonstrated a method for DECT-based ion-range prediction that properly accounts for tissue mixing.
• Showcased convenient mixing and averaging properties by linking relative stopping number to DECT-derived relative cross section.
• Suggested a maximum uncertainty below [Formula: see text] for stopping-power ratio prediction in arbitrary human tissue mixtures.

Conclusions:

• The proposed modification enables accurate ion-range prediction in mixed tissues using DECT.
• This approach enhances the clinical applicability of DECT for proton and ion therapy treatment planning.
• The method provides a robust framework for managing tissue heterogeneities, reducing range uncertainties.