Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy

Area of Science:

• Medical Physics
• Radiotherapy
• Image Processing

Background:

• Accurate material characterization is crucial for Monte Carlo (MC) based dose planning in particle therapy.
• Traditional single energy CT (SECT) methods have limitations in precisely determining tissue properties.
• Dual energy CT (DECT) offers potential for improved material decomposition and accuracy.

Purpose of the Study:

• To introduce a novel method for predicting tissue mass density and elemental mass fractions using DECT data.
• To evaluate the accuracy of this DECT method for MC-based dose planning.
• To compare the performance of the DECT method against SECT for predicting key parameters like stopping power ratios (SPR).

Main Methods:

• Calculated relative electron density (ϱ(e)) and effective atomic number (Z(eff)) for 71 tissue compositions.
• Derived mass density using a linear fit of ϱ(e) and predicted elemental mass fractions from ϱ(e) and Z(eff).
• Analyzed differences in mass density, I-value, and SPR compared to ground truth; conducted dose studies with proton and carbon ions.

Main Results:

• DECT significantly reduced mean deviations in mass density for soft tissue from 0.5±0.6% (SECT) to 0.2±0.2%.
• Maximum SPR deviations were reduced from 3.1% (SECT) to 0.7% (DECT) for soft tissue and 0.8% to 0.1% for bone.
• Elemental composition predictions, particularly for carbon and oxygen, were substantially improved with DECT.

Conclusions:

• Accurate mass density prediction using DECT is key for precise stopping power calculations in particle therapy.
• DECT demonstrated theoretical improvements in range predictions of 0.1%-2.1% compared to SECT.
• Further research is needed to assess DECT's benefits in clinical scenarios with image artifacts and noise.