Public Thesis defense - ICTEAM

Synthetic 3DCT reconstruction using fluoroscopy and convolutional neural networks for patient-specific real-time image-guided proton therapy by Estelle Loÿen

External beam radiation therapy is a standard cancer treatment that uses a source of radiation to destroy the tumor. Proton therapy is a type of radiation therapy that offers a physical advantage over conventional photon therapy thanks to the very localised dose deposition of protons within the body.  This decreases the risk of side effects as the dose delivered in the surrounding healthy tissue is lower. However, it also means that it is highly vulnerable to uncertainties. A variety of geometrical uncertainties may affect the accuracy of photon and proton therapy, such as respiratory motion or inter-fraction setup errors. Modern radiation therapy is generally performed using daily image guidance to reduce the uncertainty of overall tumor targeting. However, these technologies are expensive and require the installation of new dedicated devices, not all of which is suitable for proton therapy.

Motivated by the ease of acquiring x-rays projections in the treatment room and the need to have a 3DCT image to compute the radiation dose deposition, this thesis explores the use of artificial intelligence to reconstruct a 3DCT image from a fluoroscopy image. The research approach taken in this thesis can be divided into three main contributions. The first contribution implements a data augmentation tool to overcome the lack of medical data available to train and validate neural networks. The second contribution focuses on the design of a methodology for reconstructing a 3DCT image from a projection radiography using a patient-specific training of a convolutional neural network. The third contribution deals with the use of these images in a proton therapy treatment. In each of these last two contributions, a base case and two variants are studied. The aim of the variants is to evaluate and compare the robustness of different training methods to events that may occur in the clinic.

Jury’s members:

Prof. Benoît Macq (UCLouvain), supervisor

Prof. Laurent Francis (UCLouvain), chairperson

Prof. Christophe De Vleeschouwer (UCLouvain), secretary

Dr Damien Dasnoy-Sumell (UCLouvain)

Prof. John Lee (UCLouvain)

Prof. Vincent Gregoire (Centre Léon Bérard, France)

Dr. Guillaume Janssens (IBA, Belgique)

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