Accurate relative stopping power prediction from dual energy CT for proton therapy: Methodology and experimental validation

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    Abstract

    Proton therapy is part of radiotherapy and increasingly applied in treatment of cancer, especially for children and patients with tumours in the head and neck region. With proton therapy the tumour can be irradiated with less damage to the surrounding healthy tissues and critical structures compared to irradiation with photons. To optimally exploit this benefit of protons, the energy transferred by the protons to the tissues (the dose) must be calculated very accurately. For this, the specific energy loss of the protons for each tissue is determined based on imaging through x-ray computed tomography (CT). In clinical practice, a phenomenological model is used based on an image obtained with a single x-ray spectrum (single energy CT, SECT). The predictions of this model are not patient specific and very inaccurate for materials which differ in composition and density from the materials used for determination of the model parameters. We have developed a method using two x-ray spectra (dual energy CT, DECT). With this method the electron densities and effective atomic numbers, which determine the specific energy loss of protons in a material, are derived from two images on basis of fundamental theory of the interactions of x-rays. This method provides patient specific predictions with an accuracy better than 2%. This is a large improvement in accuracy and stability of the method with respect to the clinically applied SECT method and can lead to clinical benefit for proton therapy.
    Translated title of the contributionNauwkeurige relatieve stoppingskracht voorspelling van protonen op basis van tweevoudige energie CT voor protonentherapie: Methodologie en experimentele validatie
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • University of Groningen
    Supervisors/Advisors
    • Brandenburg, Sytze, Supervisor
    • van der Graaf, Emiel, Co-supervisor
    • Greuter, Marcel, Co-supervisor
    Award date24-Nov-2017
    Place of Publication[Groningen]
    Publisher
    Print ISBNs978-94-6233-764-0
    Electronic ISBNs978-94-6233-765-7
    Publication statusPublished - 2017

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