Estimating the 4D Respiratory Lung Motion by Spatiotemporal Registration and Building Super-Resolution Image
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Summary
A unified approach to estimate the respiratory lung motion with two iterative steps using a new spatiotemporal registration algorithm to align all phase images of 4D-CT onto a high-resolution group-mean image in the common space and achieves more accurate and consistent results in lung motion estimation than all other state-of-the-art approaches.
- Type
- article
- Published
- 2011-09-18
- Cited by
- 21
- References
- 10
- OpenAlex
- https://openalex.org/W1594938527
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2260531
Keywords
Computer science, Artificial intelligence, Computer vision, Image registration, Motion estimation
References
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- Unsupervised learning of an Atlas from unlabeled point-sets
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- Unbiased diffeomorphic atlas construction for computational anatomy.
- The management of respiratory motion in radiation oncology report of AAPM Task Group 76.
- Generalizing the Nonlocal-Means to Super-Resolution Reconstruction
- Statistical Modeling of 4D Respiratory Lung Motion Using Diffeomorphic Image Registration
- Medical Image Computing and Computer-Assisted Intervention — MICCAI’98
- Muliscale Vessel Enhancement Filtering
Cited by
- A Symmetric 4D Registration Algorithm for Respiratory Motion Modeling
- Regional pulmonary function analysis using image registration and 4DCT
- Improved Reconstruction of 4D-MR Images by Motion Predictions
- Estimating Dynamic Lung Images from High-Dimension Chest Surface Motion Using 4D Statistical Model
- Tensor-based Dictionary Learning for Dynamic Tomographic Reconstruction
- Feature-aligned 4D spatiotemporal image registration
- Improving image-guided radiation therapy of lung cancer by reconstructing 4D-CT from a single free-breathing 3D-CT on the treatment day.
- A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging
- Non-rigid registration of 2-D/3-D dynamic data with feature alignment
- Deformable image registration applied to lung SBRT: Usefulness and limitations.
- Interleaved 3D-CNNs for Joint Segmentation of Small-Volume Structures in Head and Neck CT Images
- Non-rigid point cloud registration based lung motion estimation using tangent-plane distance
- Computational and statistical methods for trajectory analysis in a Riemannian geometry setting. (Méthodes numériques et statistiques pour l'analyse de trajectoire dans un cadre de geométrie Riemannienne)
- A personalized image-guided intervention system for peripheral lung cancer on patient-specific respiratory motion model
- A novel population-characteristic weighted sparse model for accurate respiratory motion prediction in CT-guided lung cancer interventions
- Prediction of the Progression of Subcortical Brain Structures in Alzheimer's Disease from Baseline
- Intensity-Based Registration for Lung Motion Estimation
- Estimating Lung Respiratory Motion Using Combined Global and Local Statistical Models
- Learning and predicting respiratory motion from 4D CT lung images
- Non-rigid Registration of 2-d/3-d Dynamic Data with Feature Alignment Secondly, I Sincerely Thank Professor Hongchao Zhang, Professor Lu Peng for Being a Part of My Committee. Thirdly, Much Help from My Labmates, List of Figures
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