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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Conformal energy modulator design optimization using deep learning for FLASH proton therapy treatment planning

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Jost-Jongen, Elisa ULiège
Promotor(s) : Phillips, Christophe ULiège
Date of defense : 26-Jun-2023/27-Jun-2023 • Permalink : http://hdl.handle.net/2268.2/17725
Details
Title : Conformal energy modulator design optimization using deep learning for FLASH proton therapy treatment planning
Author : Jost-Jongen, Elisa ULiège
Date of defense  : 26-Jun-2023/27-Jun-2023
Advisor(s) : Phillips, Christophe ULiège
Committee's member(s) : Louveaux, Quentin ULiège
Redouté, Jean-Michel ULiège
Labarbe, Rudi 
Language : English
Keywords : [en] Flash proton therapy
[en] Artificial intelligence
Discipline(s) : Engineering, computing & technology > Civil engineering
Target public : Researchers
Professionals of domain
Student
Complementary URL : http://opentps.org/
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil biomédical, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] Radiotherapy is a method used to treat cancers based on the utilization of photons to
target and destroy cancer cells. One of the radiotherapy techniques called proton ther-
apy uses the energy released by protons to kill cancer cells. Compared to conventional
radiotherapy that uses photons, proton therapy is known to spare more healthy tissues
surrounding the tumor. This advantage stems from the particular dose (amount of energy
absorbed per kg) profile of protons in matter called the Bragg peak. When proton therapy
is delivered at very high dose rates (>40 Gy/s), this technique is called FLASH proton
therapy. It has the advantage of sparing even more healthy tissues.
The extra short delivery duration of FLASH proton therapy makes proton adminis-
tration technically challenging and some delivery methods must be adapted. Importantly,
the technique to control the depth dose distribution within the tumor must be entirely
rethought. To address this, a new device must be added to the trajectory of the proton
beams. This new piece named a Conformal Energy Modulator, is made of a square base
on top of which spikes of different heights rise. Its configuration must be tuned accord-
ing to the tumor shape. The CEM optimization can be achieved by treatment planning
systems but current solutions either lack accuracy or take too long to compute.
In this master’s thesis, a new method to optimize the CEM quickly and accurately is
proposed, relying on an artificial intelligence model called Convolutional Neural Network.
This thesis is divided into 5 main parts. The first part introduces how proton beams
are generated and delivered to the patients, how protons interact with matter, and why
their dose profile is so interesting from a treatment point of view. Additionally, the chal-
lenges posed by the FLASH concept and the required modifications for proton therapy
administration to accommodate FLASH PT are discussed. The second part is related to
the data acquisition needed for the AI model training. It is performed with a treatment
planning system that simulates FLASH PT with the CEM and the resulting dose dis-
tribution. The CEMs profiles will serve as outputs and their corresponding dose maps
will serve as inputs of the AI model. The third part introduces the concepts of artificial
intelligence, neural networks, and convolutional neural networks. The choice of the spe-
cific AI model to solve this problem is detailed as well as its architecture. The fourth
part discusses the training of the model. The model’s ability to predict a CEM given
a dose map is evaluated using validation and test sets. Lastly, the performance of the
predicted CEMs by the model compared to the initial CEMs of the data set is evalu-
ated through a comparison of their dose maps resulting from FLASH PT simulations.
The dose maps comparison is performed with dose-volume histograms and Gamma index
evaluation. This comparison highlights the significant similarities observed in the dose
maps, indicating the model’s excellent training and successful CEM optimization.


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Author

  • Jost-Jongen, Elisa ULiège Université de Liège > Master ing. civ. biomed., à fin.

Promotor(s)

Committee's member(s)

  • Louveaux, Quentin ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète
    ORBi View his publications on ORBi
  • Redouté, Jean-Michel ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes microélectroniques intégrés
    ORBi View his publications on ORBi
  • Labarbe, Rudi
  • Total number of views 117
  • Total number of downloads 172










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