Electron density and effective atomic number estimation in a maximum a posteriori framework for dual-energy computed...

Area of Science:

• Medical Physics
• Radiotherapy
• Image Analysis

Background:

• Accurate estimation of physical parameters like electron density and effective atomic number from dual-energy CT (DECT) is crucial for precise radiotherapy treatment planning.
• The proton stopping power (SPR) is a key quantity for dose calculation in radiotherapy, requiring robust estimation from DECT data.
• Existing DECT calibration methods can be sensitive to noise and biases in CT data.

Purpose of the Study:

• To adapt the Bourque et al. stoichiometric calibration method for DECT into a maximum a posteriori (MAP) framework.
• To enhance the robustness of electron density and effective atomic number estimation against noise and biases in DECT data for human tissues.
• To improve the precision of radiotherapy-related parameters such as proton stopping power (SPR).

Main Methods:

• The MAP framework constrains electron density and effective atomic number variations within natural human tissue ranges while maximizing data fidelity.
• The adapted method was compared against the original stoichiometric method using simulated CT data with Gaussian noise.
• Quantitative accuracy was validated using a Gammex 467 phantom and two clinical DECT datasets.

Main Results:

• Theoretical analysis showed root-mean-square error reductions for electron density (2.3% to 1.5%), effective atomic number (5.7% to 3.2%), and SPR (2.8% to 1.7%).
• Experimental validation demonstrated reduced standard deviations by factors of 1.4 (electron density), 2.7 (effective atomic number), and 1.9 (SPR) with preserved accuracy.
• Clinical datasets showed noise reduction and increased robustness to artifacts, with statistically significant reductions in standard deviation for all parameters using the MAP framework.

Conclusions:

• The MAP framework provides more accurate and precise estimates of electron density and SPR compared to the original method.
• This approach effectively limits noise propagation from DECT data to radiotherapy parameters, enhancing dose calculation precision.
• The MAP methodology offers more precise effective atomic number estimates and is adaptable for various DECT-based tissue characterization applications.