Laboratory for Advanced Nuclear Nonproliferation and Safety

Prompt Gamma Imaging

Background

Proton therapy uses heavy charged particles to irradiate unhealthy tissue. Recently, it has begun to gain popularity as it looks to take advantage of the lower integral dose to the patient, the well-defined range and sharp dose fall-off characteristic of heavy charged particle interactions.  In contrast to photon and electron beams, proton beams deliver a relatively constant and minimal dose to the surface and superficial tissue, depositing the majority of their dose near the incident particles’ end of range. The velocity of the proton particle is much higher near the surface of the target object than in the deeper regions.  Based on the Bethe–Bloch formula, the energy deposited per unit length is inversely proportional to the square of the particle velocity. Thus the proton particles deposit most of the dose in the Bragg peak region just before reaching their end of range. [7] Relatively no dose is distributed to tissue beyond the peak depth or distal-edge of the Bragg peak, allowing the Bragg peak to be placed directly over a target region by adjusting the energy of the proton beam.
An ideal proton treatment plan would align the Bragg peak with the target tissue, and the distal-edge of the Bragg peak could be placed immediately in front of OAR.  Unfortunately, this is not current practice due to the range uncertainty and the dramatic change in delivered dose resulting from minimal changes to proton range. Uncertainties in the exact position of the Bragg peak result from a combination of organ motion, setup and anatomical variations, dose calculation approximations and biological considerations. [8] Accurate proton treatment planning requires the use of the tissues’ relative stopping power (RSP), which is the mean energy loss of protons per unit path length of the material relative to that of water.  This is because the proton interactions are dependent on Coulomb interactions in addition to being affected by the chemical composition of the materials and their electron density.
While knowledge of the potential advantages for proton therapy due to the unique interaction characteristics of protons has been available for several decades, it has not been capitalized on in clinical applications primarily due to the large capital expense, the range uncertainty and long reconstruction computing times.  [6] This has created a need for improved range verification in order to improve treatment planning and dosimetry for proton therapy. There are currently a variety of methods of proton range verification being investigated. These include both direct measurements, such as point dose, range probe, and proton radiography/ tomography; calculations based on indirect measurements, such as prompt gamma imaging, PET imaging, MRI imaging, and hybrid imaging systems as well as improved reconstruction algorithms.  [6]

Preliminary MC Simulation of PG Generation

In the preliminary simulations MCNP6 was used to simulate PG emission spectra generated from proton induced nuclear reactions in medium of varying composition of carbon, oxygen, calcium and nitrogen, the predominant elements found in human tissue.  The relative peak intensities at discrete energies predicted by MCNP6 were compared to the corresponding atomic composition of the medium.

5 Cell phantom
5 Cell phantom taraget cell 14

Figure1: Left, Image of MCNP6 target phantom generated by VisEd. Right, Image of MCNP6 target phantom highlighting material insert from VisEd.

As output, energy spectra were generated from the PGs that originate from within and escape the specified cell of interest of the phantom: the solid green cell depicted in the phantom, Fig 1b. The results show a good general agreement with experimentally measured values reported by other investigators using MCNPX. However, unexpected divergence from experimental spectra was noted in the peak intensities for some cases depending on the source of the cross-section data when using compiled proton table libraries vs. physics models built into MCNP6.  While the use of proton cross-section libraries is generally recommended when available, these libraries lack data for several less abundant isotopes.   This limits the range of their applicability and forces the simulations to rely on physics models for reactions with natural atomic compositions.

4figures

Figure 2: Escaped PG energy spectra (a) natural oxygen, (b) elemental oxygen, (c) natural calcium and (d) elemental calcium.

MCNP6 Model Limitations

            In MCNP6 different cross section libraries are used for the particle-production data corresponding to how the material data cards are coded. When the material is coded as elemental calcium (020000) the particle-production data for protons being used is from 20000.62c library.  When the target material is coded as specific calcium isotopes (020AAA) the particle-production data for protons being used is the corresponding 20AAA.80c library.  The tabulated neutron and photon creation counts and the PG spectra that result from the two different data libraries are not in agreement. (Note: The Ca-Elemental spectra most closely correspond to previously published spectra from MCNPX.)

Current Research

This research will evaluate the secondary prompt gamma (PG) yield from proton therapy at high characteristic energies from MC model simulations and experimental data.  Recent studies indicate that target composition influences PG characteristic energy and yield, and the quantification of PG may be used to offer real-time dose verification for proton therapy. As these measurements have not been previously performed specifically for prompt gamma analysis, experimental data will be collected and MC simulations will be created to evaluate the characteristic measurements and total yield of secondary PG emitted from a target in the 2-7 MeV range from a proton therapy beam. The results from 4 different beam energies, in several target materials will be compared in order to evaluate the influence of the incident energy and the target material on the PG energy spectra.

Research Questions:

  1. What is the absolute yield of secondary PG from a proton beam at clinically relevant energies?
  2. Can target material or the RSP be determined based on the absolute yield of the secondary PG from a proton beam?
  3. Can location specific energy spectra of the secondary PG from a proton beam be used to determine target material or the RSP along the proton path?

MC Simulation of PG Generation

In the proposed series of simulations the target materials are modeled with various mono-energetic incident proton beams.  These models will be used to determine (a) the PG energy spectra from each target and energy combination and (b) the proton range within the target. The PG energy spectra will be generated by running various Monte Carlo simulation software packages: MCNPX and MCNP6. The range data will then be compared with the projected proton range determined by SRIM. The absolute PG yield and the PG relative peak intensities at discrete energies predicted by the MCNP codes will be compared to similar data previously published and our own experimental results.

Experimental Verification

As the protons move through the target material they undergo a number of interactions.  Some of these interactions result in the release of PGs, which are the result of nuclei excitation along the proton path. Current simulation work is limited by inadequate cross-section data for proton iterations at various incident energies. Experimental cross-section measurements of additional natural isotopes would provide clarification to resolve the current discrepancy and enable more accurate simulation and progress with further calculations. This information could be added to the MCNP cross-section libraries and incorporated to provide more accurate image reconstructions and therapy treatment planning range calculations.

In order to benchmark our MCNP simulation results, we are proposing an experimental measurement of the PG emitted from various targets composed of carbon, oxygen, calcium and hydrogen. The resulting PG energy spectra ratios would then be compared to those produced by the MCNP simulations.

References

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    1. SRIM Tables (2013): (http://www.srim.org/SRIM/SRIMLEGL.htm)