Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy.
| Autor: | Thummerer A; Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands., Seller Oria C; Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands., Zaffino P; Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy., Visser S; Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands., Meijers A; Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.; Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland., Guterres Marmitt G; Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands., Wijsman R; Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands., Seco J; Department of Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany.; Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany., Langendijk JA; Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands., Knopf AC; Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.; Department I of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, Germany., Spadea MF; Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy., Both S; Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Medical physics [Med Phys] 2022 Nov; Vol. 49 (11), pp. 6824-6839. Date of Electronic Publication: 2022 Aug 27. |
| DOI: | 10.1002/mp.15930 |
| Abstrakt: | 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 D 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. (© 2022 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|
Plný text