Probabilistic treatment planning for proton therapy with polynomial chaos expansion methods
Supervisor TU Delft: Martin van Gijzen, Zoltan Perko and Danny Lathouwers
Site of the project:
TU Delft, HollandPTC and ErasmusMC
Holland Protonen Therapie Centrum
2629 JH Delft
Erasmus Medisch Centrum
Medical Physics, Radiation Oncology
Doctor Molewaterplein 40
3015 GD Rotterdam
Supervisors HollandPTC / ErasmusMC: Steven Habraken and Sebastiaan Breedveld
start of the project: January 2020
Summary of the master project:
Summary of the master project: Radiotherapy is an important treatment type for patients with cancer. An advantage of state of the art proton therapy with respect to traditional photon therapy is the spatial energy deposition of protons, which is characterized by the Bragg peak. Due to this particular energy deposition profile, the tumor can be irradiated more precisely and as a consequence more healthy tissue can be spared. An important drawback of this concept is that proton therapy is more sensitive to uncertainties like misalignment of the patient (set-up error) or small position differences of the tumor between the moment of making the CT scan and the actual irradiation moment.
Robust optimization is the current way to account for such uncertainties during which a few discrete error scenarios are included in the planning. Due to the nature of these uncertainties, probabilistic optimization, on the other hand, is more promising, since it can handle many more scenarios with their occurrence probability taken into account as well. To investigate probabilistic planning, Polynomial Chaos Expansion (PCE) methods are used in this research.
Polynomial Chaos can approximate a stochastic response, depending on for example the set-up error, by a series expansion in terms of polynomials. The advantage is that typically the PCE can be evaluated much faster than the stochastic response itself. In this research an investigation will be done on how PCE can be used with probabilistic treatment planning for proton therapy.
Last year, a master student of applied physics, Jelle Salverda, produced a large contribution in the start of this research by implementing the concept of PCE on calculating treatment plans for patients probabilistically. My goal will be to test whether his implementation is also beneficial when performed on more patients. Also, other methods of calculating these probabilities and optimization methods will be investigated in order to achieve a faster optimization, in which case, this concept could, when proved beneficial over current technologies, be tested in medical clinics in the future.
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