Danny Lathouwers' Research

Computational Reactor Physics

My present teaching

AP3341D: Nuclear Reactor Physics (together with Jan Leen Kloosterman)

AP3582: Medical Physics of Photon and Proton Therapy (together with Martijn Engelsman and Eelco Lens)

AP3323: Computational Techniques for Neutron Transport and Radiative Heat TransferI continually supervise MSc and BSc final projects.

Classes I used to teach

WB4422: Thermal Power Plants. Three classes on the Nuclear energy part

TN1301: Mechanics, Golven, Optica (MGO, together with Pieter Kruit and Aurele Adam)

TN1661: Oriëntatie Onderzoek. Responsible for RST department symposium

EE1200: Klassieke en Kwantummechanica. Computerised classes for practice

AP3141D: Environmental Physics Three classes on fission and fusion.

Student Projects

I continually supervise students in my group on Bsc and MSc in the area of computational science for reactor physics and proton therapy. Contact me if you are interested.


Novel reconstruction method for SPECT/PET reconstruction  A%20novel%20method%20for%20PET%20image%20reconstruction.pdf

Robust Treatment Planning in Proton Therapy

Treatment planning in radiotherapy comprises the optimization process of achieving the best possible plan for a specific patient where a prescribed set of criteria concerning tumor dose, tumor coverage and various dose-constraints are combined and need to be satisfied. Such optimization procedures use the patient geometry (CT-scan) and material properties as input and output the best plan. It is especially important in proton therapy planning to take uncertainties in patient positioning or proton range into account from the start. Otherwise plans may result that are not robust to such uncertainties and patients may receive deteriorated dose distributions with negative consequences (side effects from e.g. overdosage of the glands). In practice such robust plans are made by considering a set of patient displacements and proton range shifts for which a treatment plan is made where the worst case of these scenarios still meets the desired criteria (worst case optimization). This is however not an optimal approach. In the present project a probabilistic view will be used as starting point where stochastic optimization procedures will be used instead of just the few discrete scenarios. In effect one can then get an optimal treatment plan in statistical sense. We will build on a proof of principle of the method delivered a few months ago. This project is more mathematically oriented.

Recipes for Robust Treatment Planning in Proton Therapy

Intensity Modulated Proton Therapy (IMPT) uses proton pencil beams whose intensities are individually optimized, which potentially results in an improved sparing of healthy tissues surrounding the tumor compared to conventional Intensity-Modulated Radiotherapy with photons. However, IMPT is highly susceptible to inaccuracies in the patient setup, internal organ motion, and from uncertainties in the anticipated proton range, known as range errors. These inaccuracies can be taken into account by constructing a robust treatment plan using ’minimax’ optimization. Minimax optimization includes the dose for a limited number of error scenarios and optimizes the worst case value of the objective function. Recently, we derived a so-called robustness recipe for head and neck cancer treatments that yields the error scenarios that need to be included to guarantee adequate CTV coverage for a high fraction patient treatments. The purpose of this project is to extent this robustness recipe to other treatment sites and treatment groups, to investigate its inter-patient variability, and to incorporate internal organ motion.

A more general list for the complete NERA (Nuclear Energy and Radiation Applications) group to which I belong can be found on the NERA website (nera.rst.tudelft.nl).