Deep learning–based 4D‐synthetic CTs from sparse‐view CBCTs for dose calculations in adaptive proton therapy
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Authors
Thummerer, Adrian
Seller Oria, Carmen
Zaffino, Paolo
Visser, Sabine
Meijers, Arturs
Guterres Marmitt, Gabriel
Wijsman, Robin
Seco, Joao
Langendijk, Johannes Albertus
Spadea, Maria Francesca
Author (Corporation)
Publication date
27.08.2022
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01A - Journal article
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Parent work
Medical Physics
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DOI of the original publication
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Volume
49
Issue / Number
11
Pages / Duration
6824-6839
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Publisher / Publishing institution
Wiley
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Abstract
Background
Time-resolved 4D cone beam–computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations.
Purpose
In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible.
Methods
A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals.
Results
4D-sCTs resulted in average MAEs of 48.1 ± 6.5 HU (single phase) and 37.7 ± 6.2 HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3% ± 3.2% (single phase) and 94.4% ± 2.1% (average). The clinical target volume showed high agreement in D98 between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues.
Conclusion
In this study, we have investigated the accuracy of deep learning–based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.
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0094-2405
2473-4209
2473-4209
Language
English
Created during FHNW affiliation
No
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Publication status
Published
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Peer review of the complete publication
Open access category
Hybrid
License
Citation
Thummerer, A., Seller Oria, C., Zaffino, P., Visser, S., Meijers, A., Guterres Marmitt, G., Wijsman, R., Seco, J., Langendijk, J. A., Spadea, M. F., Both, S., & Knopf, A. (2022). Deep learning–based 4D‐synthetic CTs from sparse‐view CBCTs for dose calculations in adaptive proton therapy. Medical Physics, 49(11), 6824–6839. https://doi.org/10.1002/mp.15930