Technical note: Implementing MRI/CT-based elemental concentration data to Monte Carlo simulation for yielding...

Area of Science:

• Medical Physics
• Radiological Sciences
• Computational Biology

Background:

• Accurate elemental concentration and mass density are crucial for Monte Carlo (MC) simulations of proton-induced nuclear reactions.
• A new method, MRWC (magnetic resonance water content), quantifies tissue elemental concentrations and mass densities from water content data.
• This research builds upon the MRWC method for enhanced simulation accuracy.

Purpose of the Study:

• To implement MRWC-derived data as inputs for MC simulations in a virtual head phantom.
• To demonstrate the simulation of positron emitter production yields in proton therapy using the MRWC/CT method.
• To evaluate the accuracy of the MRWC/CT method compared to conventional CT-only methods.

Main Methods:

• Utilized the PHITS MC code to simulate proton therapy (140 MeV beam) on a digital head phantom (BrainWeb).
• Synthesized three input datasets: conventional CT, ideal CT (reference), and a hybrid MRWC/CT approach.
• Assessed the MRWC/CT method's performance by comparing predicted positron emitter (¹¹C, ¹⁵O) production yields against CT-only simulations.

Main Results:

• The MRWC/CT method accurately predicted ¹¹C and ¹⁵O production yield maps, closely matching reference data.
• Structural similarity index improved significantly: 0.67 to 0.87 for ¹¹C and 0.76 to 0.93 for ¹⁵O compared to CT-only.
• Post-processing normalization in MRWC/CT facilitated determination of distal fall-off positions in depth activity profiles.

Conclusions:

• The MRWC/CT method shows significant potential for more precise predictions of proton-induced positron emitter distributions in the head via MC simulations.
• This hybrid approach offers superior accuracy compared to the CT-only method for MC simulations in proton therapy.