TY - JOUR
T1 - Proton dose calculation on cone-beam computed tomography using unsupervised 3D deep learning networks
AU - Dueholm Vestergaard, Casper
AU - Vindelev Elstrøm, Ulrik
AU - Paul Muren, Ludvig
AU - Ren, Jintao
AU - Nørrevang, Ole
AU - Jensen, Kenneth
AU - Trier Taasti, Vicki
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/10
Y1 - 2024/10
N2 - Background and Purpose: Poor image quality of cone-beam computed tomography (CBCT) images can hinder proton dose calculation to assess the influence of anatomy changes. The aim of this study was to evaluate image quality and proton dose calculation accuracy of synthetic CTs generated from CBCT using unsupervised 3D deep-learning networks. Materials and methods: A total of 102 head-and-neck cancer patients were used to train (N=82) and test (N=20) i) a cycle-consistent generative adversarial network, ii) a contrastive unpaired translation, and iii) a fusion of the two (CycleCUT). For patients in the test set, a repeat CT was deformably registered to a same-day CBCT to create a ground-truth CT for comparison. The proton plan was re-calculated on the ground-truth CT and synthetic CTs. The image quality of the synthetic CTs was evaluated using peak signal-to-noise ratio, structural similarity index measure, mean error, and mean absolute error (MAE). Proton dose calculation accuracy was assessed through 3D gamma analysis and dose-volume-histogram parameters. Results: All synthetic CTs accurately preserved the CBCT anatomy (verified by visual inspection) while improving the image quality. The CycleCUT network had slightly improved image quality compared to the other networks (MAE in body: 53 Hounsfield units (HU) vs. 54/55 HU). All networks had similar proton dose calculation accuracy with gamma passing rate above 97%. Conclusions: All three evaluated networks generated synthetic CT images with dose distributions comparable to those of conventional fan-beam CT. The synthetic CT generation was fast, making all networks feasible for adaptive proton therapy.
AB - Background and Purpose: Poor image quality of cone-beam computed tomography (CBCT) images can hinder proton dose calculation to assess the influence of anatomy changes. The aim of this study was to evaluate image quality and proton dose calculation accuracy of synthetic CTs generated from CBCT using unsupervised 3D deep-learning networks. Materials and methods: A total of 102 head-and-neck cancer patients were used to train (N=82) and test (N=20) i) a cycle-consistent generative adversarial network, ii) a contrastive unpaired translation, and iii) a fusion of the two (CycleCUT). For patients in the test set, a repeat CT was deformably registered to a same-day CBCT to create a ground-truth CT for comparison. The proton plan was re-calculated on the ground-truth CT and synthetic CTs. The image quality of the synthetic CTs was evaluated using peak signal-to-noise ratio, structural similarity index measure, mean error, and mean absolute error (MAE). Proton dose calculation accuracy was assessed through 3D gamma analysis and dose-volume-histogram parameters. Results: All synthetic CTs accurately preserved the CBCT anatomy (verified by visual inspection) while improving the image quality. The CycleCUT network had slightly improved image quality compared to the other networks (MAE in body: 53 Hounsfield units (HU) vs. 54/55 HU). All networks had similar proton dose calculation accuracy with gamma passing rate above 97%. Conclusions: All three evaluated networks generated synthetic CT images with dose distributions comparable to those of conventional fan-beam CT. The synthetic CT generation was fast, making all networks feasible for adaptive proton therapy.
KW - Adaptive proton therapy
KW - Cone-beam CT
KW - Deep-learning
KW - Image quality improvement
KW - Proton dose calculation
KW - Synthetic CT
UR - http://www.scopus.com/inward/record.url?scp=85207901516&partnerID=8YFLogxK
U2 - 10.1016/j.phro.2024.100658
DO - 10.1016/j.phro.2024.100658
M3 - Journal article
C2 - 39534276
AN - SCOPUS:85207901516
SN - 2405-6316
VL - 32
JO - Physics and Imaging in Radiation Oncology
JF - Physics and Imaging in Radiation Oncology
M1 - 100658
ER -