Ion Computed Tomography (Landry)

Department / Institute
Department of Radiation Oncology, University Hospital of the LMU Munich
Subject area
Ion Computed Tomography
Project title
Comparison of ion imaging performance between protons and helium ions
Name of supervisor
Prof. Dr. Guillaume Landry
Number of open positions
Language requirements
Proficiency in English
Academic requirements
Master's Degree; good programming knowledge (c++, python) and knowledge of the physics of particle therapy and imaging
Project time plan
36 months

Project description

Particle therapy exploits the finite range and inverse depth dose profile of charged particles (protons, helium ions or carbon ions) for highly conformal cancer radiotherapy. Accurate treatment planning requires an accurate map of the patient’s relative stopping power (RSP). Current approaches utilize either a stochiometric conversion from single-energy x-ray Computed Tomography (CT) or more sophisticated methods based on dual-energy x-ray CT to obtain the patient specific RSP maps. Particle imaging has been developed as an alternative planning and verification tool. Thereby, the energy loss of charged particles that traversed the patient fully is used to reconstruct radiographic projections (particle radiography; PRad) of the patient’s water equivalent thickness or, if a tomography is acquired, directly the RSP map (particle CT; PCT). Currently several prototype particle imaging prototypes exist, and dedicated image reconstruction software has been developed, with promising results presented in literature [1-5].

Most often, particle imaging is performed with protons following the more widespread availability of proton therapy centers over heavier ion centers. Helium ions have reduced multiple scattering inside the patient when compared to protons, though. At the same imaging dose given to the patient, helium ions therefore can offer improved spatial resolution. The recently commenced Helium ion therapy program at the Heidelberg Ion-Beam Therapy Center (HIT, Heidelberg) has sparked rising interest also in particle imaging with helium ions. Existing work with a prototype scanner [6] has already demonstrated the high quality of Helium ion CT scans (HeCT) compared to proton CT (pCT), and x-ray CT modalities [2,3]. This work also revealed technical differences between pCT and HeCT scans with the scanner: artifacts typically present with the employed prototype were reduced for HeCT compared to pCT. This technical difference can give insight into the origin of these artifacts, which are a key limiting factor for obtaining the theoretical accuracy of the prototype system. Identifying the cause of the artifacts therefore could lay the basis for improved PCT technology towards clinical application.

Within this masters thesis project, first a comparison of various available PCT and PRad reconstruction algorithms will be performed to identify a solid basis for comparison between modalities. Further, a detector based comparison between proton and helium ion imaging with the prototype particle imaging scanner will be conducted, with the aim to validate recent findings from Monte Carlo simulations [7] and identify possible improvements to the detector design.

This project is embedded within an international research collaboration between institutions at Munich (GER), Darmstadt (GER), Heidelberg (GER), Chicago (USA), Santa Cruz (USA) and Loma Linda (USA). The successful candidate will have access to a vast database of experimental proton and helium ion imaging runs of various plastic and animal tissue phantoms acquired at the Northwestern Medicine Chicago Proton Center, and the Heidelberg Ion-Beam Therapy Center with a state-of-the-art prototype particle imaging system. An extensive infrastructure of simulation environments, and image reconstruction, as well as analysis software is already available.

We seek a highly motivated individual, interested in working on state-of-the-art technology within a multi-national team. The candidate should have experience in programming, preferentially in C++ and Python. Prior experience in medical imaging, particle physics detector design, or Monte Carlo particle transport simulations would be beneficial, but is not a requirement.

[1] G. Dedes et al 2019 Phys. Med. Biol. 64 165002; doi: 10.1088/1361-6560/ab2b72
[2] Bär, E, Volz, L, Collins-Fekete, C-A, et al. Med Phys. 2022; 49: 474– 487. doi: 10.1002/mp.15283
[3] L Volz et al 2021 Phys. Med. Biol. 66 235010; doi: 10.1088/1361-6560/ac33ec
[4] C. Civinini et al. (2020) Phys. Med. Biol. 17;65(22):225012. doi: 10.1088/1361-6560/abb0c8
[5] DeJongh, DF et al. Med Phys. 2021; 48: 7998– 8009.
[6] R.P. Johnson et al. (2016) IEEE Trans Nucl Sci. 63(1): 52–60. doi: 10.1109/TNS.2015.2491918
[7] S. Götz et al. (2022) Phys. Med. Biol. 67 055003; doi: 10.1088/1361-6560/ac4fa4

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