Deep learning framework to improve the quality of cone-beam computed tomography for radiotherapy scenarios.
CBCT
adaptive radiotherapy
deep learning
noise reduction
personalized model
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
revised:
01
06
2023
received:
06
10
2022
accepted:
03
06
2023
medline:
6
12
2023
pubmed:
22
6
2023
entrez:
22
6
2023
Statut:
ppublish
Résumé
The application of cone-beam computed tomography (CBCT) in image-guided radiotherapy and adaptive radiotherapy remains limited due to its poor image quality. In this study, we aim to develop a deep learning framework to generate high-quality CBCT images for therapeutic applications. The synthetic CT (sCT) generation from the CBCT was proposed using a transformer-based network with a hybrid loss function. The network was trained and validated using the data from 176 patients to produce a general model that can be extensively applied to enhance CBCT images. After the first therapy, each patient can receive paired CBCT/planning CT (pCT) scans, and the obtained data were used to fine-tune the general model for further improvement. For subsequent treatment, a patient-specific, personalized model was made available. In total, 34 patients were examined for general model testing, and another six patients who underwent rescanned pCT scan were used for personalized model training and testing. The general model decreased the mean absolute error (MAE) from 135 HU to 59 HU as compared to the CBCT. The hybrid loss function demonstrated superior performance in CT number correction and noise/artifacts reduction. The proposed transformer-based network also showed superior power in CT number correction compared to the classical convolutional neural network. The personalized model showed improvement based on the general model in some details, and the MAE was reduced from 59 HU (for the general model) to 57 HU (p < 0.05 Wilcoxon signed-rank test). We established a deep learning framework based on transformer for clinical needs. The deep learning model demonstrated potential for continuous improvement with the help of a suggested personalized training strategy compatible with the clinical workflow.
Sections du résumé
BACKGROUND
BACKGROUND
The application of cone-beam computed tomography (CBCT) in image-guided radiotherapy and adaptive radiotherapy remains limited due to its poor image quality.
PURPOSE
OBJECTIVE
In this study, we aim to develop a deep learning framework to generate high-quality CBCT images for therapeutic applications.
METHODS
METHODS
The synthetic CT (sCT) generation from the CBCT was proposed using a transformer-based network with a hybrid loss function. The network was trained and validated using the data from 176 patients to produce a general model that can be extensively applied to enhance CBCT images. After the first therapy, each patient can receive paired CBCT/planning CT (pCT) scans, and the obtained data were used to fine-tune the general model for further improvement. For subsequent treatment, a patient-specific, personalized model was made available. In total, 34 patients were examined for general model testing, and another six patients who underwent rescanned pCT scan were used for personalized model training and testing.
RESULTS
RESULTS
The general model decreased the mean absolute error (MAE) from 135 HU to 59 HU as compared to the CBCT. The hybrid loss function demonstrated superior performance in CT number correction and noise/artifacts reduction. The proposed transformer-based network also showed superior power in CT number correction compared to the classical convolutional neural network. The personalized model showed improvement based on the general model in some details, and the MAE was reduced from 59 HU (for the general model) to 57 HU (p < 0.05 Wilcoxon signed-rank test).
CONCLUSION
CONCLUSIONS
We established a deep learning framework based on transformer for clinical needs. The deep learning model demonstrated potential for continuous improvement with the help of a suggested personalized training strategy compatible with the clinical workflow.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7641-7653Subventions
Organisme : the National Natural Science Foundation of China
ID : 12205375
Organisme : Beijing Nova Program
ID : Z201100006820058
Organisme : CAMS Innovation Fund for Medical Sciences
ID : 2020-I2M-C&T-B-073
Informations de copyright
© 2023 American Association of Physicists in Medicine.
Références
Mackie TR, Kapatoes J, Ruchala K, et al. Image guidance for precise conformal radiotherapy. Int J Radiat Oncol Biol Phys. 2003;56(1):89-105.
Létourneau D, Wong JW, Oldham M, et al. Cone-beam-CT guided radiation therapy: technical implementation. Radiother Oncol. 2005;75(3):279-286.
Verellen D, Ridder MD, Linthout N, Tournel K, Soete G, Storme G. Innovations in image-guided radiotherapy. Nat Rev Cancer. 2007;7(12):949-960.
Yan D, Vicini F, Wong J, Martinez A. Adaptive radiation therapy. Phys Med Biol. 1997;42(1):123-132.
Lim-Reinders S, Keller BM, Al-Ward S, Sahgal A, Kim A. Online adaptive radiation therapy. Int J Radiat Oncol Biol Phys. 2017;99(4):994-1003.
Albertini F, Matter M, Nenoff L, Zhang Y, Lomax A. Online daily adaptive proton therapy. Br J Radiol. 2019;93(1107):20190594.
Kurz C, Dedes G, Resch A, et al. Comparing cone-beam CT intensity correction methods for dose recalculation in adaptive intensity-modulated photon and proton therapy for head and neck cancer. Acta Oncol (Madr). 2015;54(9):1651-1657.
Landry G, Nijhuis R, Dedes G, et al. Investigating CT to CBCT image registration for head and neck proton therapy as a tool for daily dose recalculation. Med Phys. 2015;42(3):1354-1366.
Zbijewski W, Beekman FJ. Efficient Monte Carlo based scatter artifact reduction in cone-beam micro-CT. IEEE Trans Med Imaging. 2006;25(7):817-827.
Jia X, Yan H, Cervino L, Folkerts M, Jiang SB. A GPU tool for efficient, accurate, and realistic simulation of cone beam CT projections. Med Phys. 2012;39(12):7368-7378.
Wang J, Li TF, Xing L. Iterative image reconstruction for CBCT using edge-preserving prior. Med Phys. 2009;36(1):252-260.
Jia X, Dong B, Lou YF, Jiang SB. GPU-based iterative cone-beam CT reconstruction using tight frame regularization. Phys Med Biol. 2011;56(13):3787-3807.
Tian Z, Jia X, Yuan KH, Pan TS, Jiang SB. Low-dose CT reconstruction via edge-preserving total variation regularization. Phys Med Biol. 2011;56(18):5949-5967.
Kida S, Nakamoto T, Nakano M, et al. Cone beam computed tomography image quality improvement using a deep convolutional neural network. Cureus. 2018;10(4):e2548.
Kurz C, Maspero M, Savenije MHF, et al. CBCT correction using a cycle-consistent generative adversarial network and unpaired training to enable photon and proton dose calculation. Phys Med Biol. 2019;64(22):225004.
Nomura Y, Xu Q, Shirato H, Shimizu S, Xing L. Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network. Med Phys. 2019;46(7):3142-3155.
Yuan NM, Dyer B, Rao S, et al. Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy. Phys Med Biol. 2020;65(3):035003.
Chen LY, Liang X, Shen CY, Jiang S, Wang J. Synthetic CT generation from CBCT images via deep learning. Med Phys. 2020;47(3):1115-1125.
Lalonde A, Winey B, Verburg J, Paganetti H, Sharp GC. Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy. Phys Med Biol. 2020;65(24):245022.
Chen L, Liang X, Shen C, Nguyen D, Jiang S, Wang J. Synthetic CT generation from CBCT images via unsupervised deep learning. Phys Med Biol. 2021;66(11):115019.
Liu Y, Chen X, Zhu J, et al. A two-step method to improve image quality of CBCT with phantom-based supervised and patient-based unsupervised learning strategies. Phys Med Biol. 2022;67(8):084001.
Rusanov B, Hassan GM, Reynolds M, et al. Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: a systematic review. Med Phys. 2022;49(9):6019-6054.
Yuan N, Rao S, Chen Q, Sensoy L, Qi J, Rong Y. Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following Image Gently Protocol using deep neural network. Med Phys. 2022;49(5):3263-3277.
Zhang Y, Yue N, Su M-Y, et al. Improving CBCT quality to CT level using deep learning with generative adversarial network. Med Phys. 2021;48(6):2816-2826.
Liu YZ, Lei Y, Wang TH, et al. CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy. Med Phys. 2020;47(6):2472-2483.
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017; Long Beach, California, USA.
Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: transformers for image recognition at scale. arXiv preprint arXiv:201011929. 2020.
Liu Z, Lin Y, Cao Y, et al. Swin transformer: hierarchical vision transformer using shifted windows. arXiv preprint arXiv:210314030. 2021.
Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M, Transformers in vision: a survey. ACM Computing Surveys (CSUR). 2021.
Shamshad F, Khan S, Zamir SW, et al. Transformers in medical imaging: a survey. arXiv preprint arXiv:220109873. 2022.
Dalmaz O, Yurt M, Çukur T. ResViT: residual vision transformers for multi-modal medical image synthesis. IEEE Trans Med Imaging. 2022;41(10):2598-2614. doi:10.1109/TMI.2022.3167808:1-1
Kamran SA, Hossain KF, Tavakkoli A, Zuckerbrod SL, Baker SA. Vtgan: semi-supervised retinal image synthesis and disease prediction using vision transformers. Paper presented at: Proceedings of the IEEE/CVF International Conference on Computer Vision2021.
Zhang Z, Yu L, Liang X, Zhao W. TransCT XingL, : Dual-Path Transformer for Low Dose Computed Tomography. Paper presented at: International Conference on Medical Image Computing and Computer-Assisted Intervention2021.
Cao H, Wang Y, Chen J, et al. Swin-unet: unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:210505537. 2021.
Taud H, Mas J. Multilayer perceptron (MLP). Geomatic Approaches for Modeling Land Change Scenarios. Springer; 2018:451-455. In:.
Shaw P, Uszkoreit J, Vaswani A, Self-attention with relative position representations. arXiv preprint arXiv:180302155. 2018.
Chow LS, Paramesran R. Review of medical image quality assessment. Biomed Signal Process Control. 2016;27:145-154.
Kanopoulos N, Vasanthavada N, Baker RL. Design of an image edge detection filter using the Sobel operator. IEEE J Solid-State Circuits. 1988;23(2):358-367.
Ronneberger O, Fischer P, U-Net BroxT. Convolutional Networks for Biomedical Image Segmentation. Cham; 2015.
Oktay O, Schlemper J, Folgoc LL, et al. Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:180403999. 2018.
Wu W, Qu J, Cai J, Yang R. Multiresolution residual deep neural network for improving pelvic CBCT image quality. Med Phys. 2022;49(3):1522-1534.
Zhu J-Y, Park T, Isola P, Efros AA, Unpaired image-to-image translation using cycle-consistent adversarial networks Paper presented at: Proceedings of the IEEE international conference on computer vision2017.
Mukhiddin T, Lee W, Lee S, Rashid T. Research Issues on Generative Adversarial Networks and Applications Paper presented at: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp); February 19-22, 2020, 2020.