A physics guided neural network approach for dose prediction in automated radiation therapy treatment planning

Radiotherapy treatment planning is a complex and time consuming process prone to differences as result of choices of individual planners. Autoplanning systems have been introduced to both reduce the time consumption and to counteract the influence of individual planning choices. Although autoplanning generally increases performance of the...

The Biological Effects of Breathing Interplay in Proton Therapy Treatments

During proton therapy treatments of lung tumors, interplay effects occur due to the simultaneous movement of the tumor due to patient’s respiratory breathing motion and the scanning pencil beam. These effects make delivering the dose to the target challenging. In this project the biological effects of breathing interplay for proton therapy...

Improving Probabilistic Treatment Planning with Polynomial Chaos Expansion for Proton Therapy

In proton therapy, robust treatment planning is currently used to account for uncertainties in patient alignment and proton beam range. A way to overcome the limitations of robust treatment planning is to use probabilistic treatment planning, which can be computationally expensive due to the calculations of statistical measures of stochastic...

Uncertainty Quantification of the Mixing Enthalpy, Excess Heat Capacity and Gibbs Energy parameters of the LiF-KF System Using CALPHAD Modelling and Polynomial Chaos Expansion

In the recent years, there has been a growing interest in molten salt reactors as a source of energy. To ensure molten salt reactor safety, it is vital to know the thermodynamic properties of the systems involved. An investigation into the uncertainty of the mixing enthalpy, excess heat capacity and Gibbs energy parameters of the LiF-KF system...

Predicting high order Reduced Order Model coefficients for proton therapy dose approximations using deep learning

The advantage of using protons to irradiate a tumour in cancer treatment, is that the energy can be delivered very precisely to the tumour without irradiating much of the surrounding tissue. The disadvantage of this is that small displacements of a patient can result in large deviations in the planned dose delivery according to the treatment...

Zebrafish irradiation: The effects of high dose-rate radiation on zebrafish embryos

Finding a favorable cure for cancer has been one of the main clinical challenges today. Nowadays the majority of the patients is treated with radiotherapy. Recently research in to a new paradigm of radiotherapy, so called FLASH radiotherapy, has opened up a new insight in to reducing negative side effects. FLASH dose rates (>40 Gy/s) have the...

Coupled reduced order model and adjoint methodologies for proton therapy

The goal of this project is to see if we can improve the treatment plan for proton therapy by using reduced order models and adjoint theory for proton therapy. We shall use a singular value decomposition on a dose distribution matrix to obtain the modes from which we can reconstruct every dose distribution. Using adjoint methodologies for proton...

Probabilistic Treatment Planning with Polynomial Chaos Expansion for Proton Therapy

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 course of energy deposition, the tumor can be irradiated more precisely and...

Texture Analysis Feature Optimization for Intensity-Modulated Radiation Therapy

Automated Intensity-Modulated Radiation Therapy (IMRT) planning is based on a set of constraints and objectives, called a wish list. This thesis concerns automated IMRT planning with a non-smooth, non-convex, discontinuous, multivariable objective function. Specifically, constrained optimization using the Long Run High Gray-level Emphasis (LRHGE...

The feasibility of proton boron capture therapy: A computational study

Proton therapy efficiency can be described as the ratio between tumour and non-tumour dose, while the tumour receives the planned dose. This efficiency is limited by the energy deposition property of the proton. To enhance the efficiency beyond this physical limit, targeted nuclear reactions during proton therapy could be exploited. For this...

Machine learning for knowledge-based dose-volume histogram prediction in prostate cancer

Introduction:  Despite the vast amount of optimization algorithms, radiotherapy treatment planning remains a manual, time-consuming and iterative process. To increase plan standardization, we clinically use Pinnacle's autoplanner for several disease sites. However, this introduces new challenges: first, the autoplanner is not perfect and still...

Reduced Order Modelling methodologies for proton therapy applications

In proton therapy, the calculation of the dose distribution is of great importance in order to find the best treatment plan. For a treatment plan with high quality, several error scenarios are investigated, in order to come up with a general plan that suits best in these scenarios. All of the scenarios have a different simulated error, e.g., in...

Development of a 3D-printed phantom for proton therapy

Proton therapy is a relatively new technique in the field of radiation oncology. The advantage of using protons can be illustrated by the depth-dose relation, which results in a more concentrated dose at a specific depth and thus potentially less dose in the surrounding healthy tissue compared to conventional photon therapy. However, this depth...