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doctoral thesis

Beam Separator for FLASH Electron Therapy Facilities

Korchevnyuk, Vera  
2024

FLASH radiotherapy (FLASH-RT) is an innovation in cancer therapy characterized by ultra-high radiation doses within a tenth of a second. This method is promising because it reduces the side effects of radiotherapy by sparing healthy tissue while effectively attacking cancer cells. Consequently, FLASH-RT has the potential to significantly improve patient treatment. Treatment times are shortened, and long-term side effects are minimized. Currently, there are no clinical facilities that can use FLASH-RT to treat deep-seated and large-volume tumors. In response to this need, the Deep Electron FLASH Therapy (DEFT) project was launched in collaboration between CERN and Lausanne University Hospital (CHUV). The DEFT facility aims to utilize technologies from the Compact Linear Collider (CLIC) to create a compact system capable of delivering high-energy electron beams within FLASH time windows. A prerequisite for the DEFT facility is a device to separate and control high-energy electron beams. Existing beam separation solutions are inadequate for the requirements of the DEFT facility, either due to their slow response times or their complexity, size and cost. In this work, a method inspired by FODO (Focusing-Defocusing) cells is presented for the design of a Beam Separator that simultaneously separates and focuses electron beams, and then adapted to the boundary conditions of the Beam Separator of the DEFT facility. A beam-based optimization provides the optimal magnet specifications for the separator. In addition, this thesis discusses the post-production steps required, including device characterization and strategies to control dynamic effects during operation. The aim is to ensure that the Beam Separator functions as intended in a clinical environment, thereby contributing to the overall success of the DEFT facility.

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EPFL_TH11277.pdf

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Main Document

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http://purl.org/coar/version/c_be7fb7dd8ff6fe43

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openaccess

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25.19 MB

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18a85e08f53dde2819c5b9b022ea386b

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