Range prediction for tissue mixtures based on dual-energy CT

TL;DR

This paper improves tissue mixture range prediction in dual-energy CT for proton therapy by incorporating proper mixing behavior, achieving less than 1% uncertainty in stopping-power ratio predictions for human tissues.

Contribution

It introduces a modified DECT-based method that accounts for tissue mixtures and heterogeneities, enhancing accuracy in range prediction for clinical proton therapy.

Findings

Maximum uncertainty of <1% in stopping-power ratio prediction for tissue mixtures

Method relates relative stopping number to DECT-derived cross section

Approach effectively handles tissue heterogeneities in treatment planning

Abstract

The use of dual-energy CT (DECT) potentially decreases range uncertainties in proton and ion therapy treatment planning via determination of the involved physical target quantities. For eventual clinical application, the correct treatment of tissue mixtures and heterogeneities is an essential feature, as they naturally occur within a patient's CT. Here, we present how existing methods for DECT-based ion-range prediction can be modified in order to incorporate proper mixing behavior on several structural levels. Our approach is based on the factorization of the stopping-power ratio into the relative electron density and the relative stopping number. The latter is confined for tissue between about 0.95 and 1.02 at a therapeutic beam energy of 200 MeV/u and depends on the I-value. We show that convenient mixing and averaging properties arise by relating the relative stopping number to the relative cross section obtained by DECT. From this, a maximum uncertainty of the stopping-power ratio prediction below 1% is suggested for arbitrary mixtures of human body tissues.