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Updated: Oct 7, 2026

Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition
Published on: March 11, 2021
Characterization of animal tissues for Monte Carlo simulations in ion beam therapy
Liu Xiaoli1,2,3, Michael F Moyers1,2,3
1Department of Medical Physics, Shanghai Proton and Heavy Ion Center, Shanghai, China.
Purpose:
To obtain better elemental compositions (ECs), physical densities (PDs), and relative linear stopping powers (RLSPs) of tissues scanned by single or dual energy computed tomography for use by Monte Carlo (MC) simulations of light ion beam therapy.
Methods And Materials:
The RLSPs of 12 animal tissue samples were measured using several different energy proton and carbon ion beams. The EC of each sample was analyzed using the following modern laboratory techniques: Inductively coupled plasma optical emission spectrometry (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), organic elemental analysis (OEA), and ion chromatography (IC). RLSPs were calculated from both measured ECs and literature ECs and then compared with measured RLSPs. All samples underwent single-energy CT (SECT) and dual-energy CT (DECT) scanning after which a dual-energy index (DEI) was derived. A mapping from computed tomography number (CTN) and DEI to PD was performed.
Results:
For each tissue sample, the measured RLSPs showed no significant dependence on beam energy or ion species, with all values agreeing to within ± 1% except for lung (within ± 2%). RLSPs directly measured and those calculated using measured PDs and ECs agreed within ± 2% except for lung. Measured RLSPs were slightly closer to values from laboratory-determined ECs than to literature ECs.
Conclusions:
RLSP measurements were independent of beam energy and ion species. The main determinants of accurate CTN to RLSP conversion are the PDs and fractional hydrogen content of the tissues. The accuracy of PD and EC for input into Monte Carlo transport calculations can be improved by combining DEI and scaled CTN.
