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primary topic: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy

Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy
type: Article
abstract: Adaptive radiotherapy relies on daily imaging to capture anatomical changes during treatment, yet current modalities such as cone-beam CT (CBCT) and adaptation techniques using deformable image registration (DIR) occasionally lack the required accuracy. CBCT improvements in acquisition and reconstruction remain limited in routine use, while DIR struggles with large anatomical changes and lacks robust quality assurance. These shortcomings motivate the search for alternative adaptation approaches. As a potential solution, this thesis investigates synthetic CT (synCT) images generated using a CycleGAN network. SynCTs demonstrated improved image quality compared with CBCT and closer agreement with daily anatomy than DIR-based CT, supporting their use in clinically relevant tasks. Treatment planning on synCTs achieved similar target coverage and sparing of organs at risk to other adaptation methods, and enabled training of organ at risk segmentation networks in the absence of high-quality annotated CBCT datasets. These findings suggest that deep learning-based synCT images can strengthen adaptive radiotherapy workflows by overcoming several limitations of existing methods. While further validation on larger and more diverse patient cohorts is needed, synCT demonstrates potential as a clinically useful modality, bridging the gap between current practice and the goal of reliable patient-specific daily treatment adaptation.
date: 2025
author list: authors
type: EPrint
type: DoctoralThesisEPrint
is part of: repository
has document: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
has document: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (Other)
subject: 500 Naturwissenschaften und Mathematik
subject: 530 Physik
subject: 600 Technik, Medizin, angewandte Wissenschaften
subject: 610 Medizin

Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
type: Document
has file: Dissertation_Stanic.pdf
has part: Dissertation_Stanic.pdf

Goran Stanic
type: Person
given name: Goran
family name: Stanic

Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (Other)
type: Document
is version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
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Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (Other)
type: Document
is version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
is volatile version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
ispreview thumbnail version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
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Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (Other)
type: Document
is version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
is volatile version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
ismedium thumbnail version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
has file: medium.jpg
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Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (Other)
type: Document
is version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
is volatile version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
issmall thumbnail version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
has file: small.jpg
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Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (Other)
type: Document
is version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
is volatile version of: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy (PDF)
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HTML Summary of #37523 Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy
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primary topic: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy

500 Naturwissenschaften und Mathematik
type: Concept
preferred label: 500 Natural sciences and mathematics @en

530 Physik
type: Concept
preferred label: 530 Physics @en

600 Technik, Medizin, angewandte Wissenschaften
type: Concept
preferred label: 600 Technology (Applied sciences) @en

610 Medizin
type: Concept
preferred label: 610 Medical sciences Medicine @en