eprintid: 37523
rev_number: 16
eprint_status: archive
userid: 9377
dir: disk0/00/03/75/23
datestamp: 2025-11-06 12:58:37
lastmod: 2025-11-06 12:58:56
status_changed: 2025-11-06 12:58:37
type: doctoralThesis
metadata_visibility: show
creators_name: Stanic, Goran
title: Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy
subjects: ddc-500
subjects: ddc-530
subjects: ddc-600
subjects: ddc-610
divisions: i-130200
adv_faculty: af-13
cterms_swd: Computertomografie
cterms_swd: Strahlentherapie
cterms_swd: Künstliche Intelligenz
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
id_scheme: DOI
id_number: 10.11588/heidok.00037523
own_urn: urn:nbn:de:bsz:16-heidok-375231
date_accepted: 2025-10-17
advisor: HASH(0x55b513087678)
language: eng
bibsort: STANICGORADEEPLEARNI2025
full_text_status: public
place_of_pub: Heidelberg
citation:   Stanic, Goran  (2025) Deep Learning-Based Synthetic CT Images for Adaptive Radiotherapy.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/37523/1/Dissertation_Stanic.pdf