Technical Note: Experimental carbon ion range verification in inhomogeneous phantoms using prompt gammas

The purpose of this study was to experimentally assess the possibility to monitor carbon ion range variations--due to tumor shift and/or elongation or shrinking--using prompt-gamma (PG) emission with inhomogeneous phantoms. Such a study is related to the development of PG monitoring techniques to be used in a carbon ion therapy context.

A 95 MeV/u carbon ion beam was used to irradiate phantoms with a variable density along the ion path to mimic the presence of bone and lung in homogeneous humanlike tissue. PG profiles were obtained after a longitudinal scan of the phantoms. A setup comprising a narrow single-slit collimator and two detectors placed at 90° with respect to the beam axis was used. The time of flight technique was applied to allow the selection between PG and background events.

Using the positions at 50% entrance and 50% falloff of the PG profiles, a quantity called prompt-gamma profile length (PGPL) is defined. It is possible to observe shifts in the PGPL when there are absolute ion range shifts as small as 1-2 mm. Quantitatively, for an ion range shift of -1.33 ± 0.46 mm (insertion of a Teflon slab), a PGPL difference of -1.93 ± 0.58 mm and -1.84 ± 1.27 mm is obtained using a BaF2 and a NaI(Tl) detector, respectively. In turn, when an ion range shift of 4.59 ± 0.42 mm (insertion of a lung-equivalent material slab) is considered, the difference is of 4.10 ± 0.54 and 4.39 ± 0.80 mm for the same detectors.

Herein, experimental evidence of the usefulness of employing PG to monitor carbon ion range using inhomogeneous phantoms is presented. Considering the homogeneous phantom as reference, the results show that the information provided by the PG emission allows for detecting ion range shifts as small as 1-2 mm. When considering the expected PG emission from an energy slice in a carbon ion therapy scenario, the experimental setup would allow to retrieve the same PGPL as the high statistics of the full experimental dataset in 58% of the times. However, this success rate increases to 93% when using a better optimized setup by means of Monte Carlo simulations.