ArticleMedical physics2019
Technical Note: Deriving ventilation imaging from 4DCT by deep convolutional neural network.
Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Incorporation of Functional Lung Imaging Into Radiation Therapy Planning in Patients With Lung Cancer: A Systematic Review and Meta-Analysis.International journal of radiation oncology, biology, physics · 2024Pooled it
- Pulmonary ventilation images from planning CT images using deep learning on locally-advanced NSCLC patients.Journal of applied clinical medical physics · 2026Article
- Investigating the impact of key algorithm parameters and patient-specific factors on the accuracy of CT ventilation imaging.Journal of applied clinical medical physics · 2026Article
- A novel method for generating a fused ventilation-perfusion map using CT-derived ventilation and FDG-PET for lung functional avoidance in radiotherapy: A feasibility study.Precision radiation oncology · 2026Article
- [Research progress on predicting radiation pneumonia based on four-dimensional computed tomography ventilation imaging in lung cancer radiotherapy].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Hybrid method for estimating lung ventilation from CT by combining intensity and motion information.Medical physics · 2025Article
- Motion and anatomy dual aware lung ventilation imaging by integrating Jacobian map and average CT image using dual path fusion network.Medical physics · 2025Article
- Advances in CT-based lung function imaging for thoracic radiotherapy.Frontiers in oncology · 2024Review
- A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI.Medical physics · 2023Article
- PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.Scientific reports · 2023Article
- A super-voxel-based method for generating surrogate lung ventilation images from CT.Frontiers in physiology · 2023Article
- Investigating the use of machine learning to generate ventilation images from CT scans.Medical physics · 2022Article
- A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison withMedical physics · 2022Article
- Artificial intelligence in functional imaging of the lung.The British journal of radiology · 2022Review
- Deep learning in structural and functional lung image analysis.The British journal of radiology · 2022Review
- Functional lung imaging in thoracic tumor radiotherapy: Application and progress.Frontiers in oncology · 2022Review
- Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy.Frontiers in oncology · 2021Article
- Technical Note: On the spatial correlation between robust CT-ventilation methods and SPECT ventilation.Medical physics · 2020Article
- Modeling the impact of out-of-phase ventilation on normal lung tissue response to radiation dose.Medical physics · 2020Article
- CT-based ventilation imaging in radiation oncology.BJR open · 2019Review
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Authors and funding
9 authors.
Funding
Abstract
purposeVentilation images can be derived from four-dimensional computed tomography (4DCT) by analyzing the change in HU values and deformable vector fields between different respiration phases of computed tomography (CT). As deformable image registration (DIR) is involved, accuracy of 4DCT-derived ventilation image is sensitive to the choice of DIR algorithms. To overcome the uncertainty associated with DIR, we develop a method based on deep convolutional neural network (CNN) to derive ventilation images directly from the 4DCT without explicit image registration.
methodsA total of 82 sets of 4DCT and ventilation images from patients with lung cancer were used in this study. In the proposed CNN architecture, the CT two-channel input data consist of CT at the end of exhale and the end of inhale phases. The first convolutional layer has 32 different kernels of size 5 × 5 × 5, followed by another eight convolutional layers each of which is equipped with an activation layer (ReLU). The loss function is the mean-squared-error (MSE) to measure the intensity difference between the predicted and reference ventilation images.
resultsThe predicted images were comparable to the label images of the test data. The similarity index, correlation coefficient, and Gamma index passing rate averaged over the tenfold cross validation were 0.880 ± 0.035, 0.874 ± 0.024, and 0.806 ± 0.014, respectively.
conclusionsThe results demonstrate that deep CNN can generate ventilation imaging from 4DCT without explicit deformable image registration, reducing the associated uncertainty.
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