Geometry Calibration & Motion Compensation
The papers in this topic, the datasets they use, the teams that focus on it, the toolkits assigned to it, and the authors with several papers here. Select any node to see its connections.
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Geometry Calibration & Motion Compensation
Team
- Bouman Group
- Computational Imaging Group
- CT Imaging Group, Department of Engineering Physics
- I-STAR Lab (Imaging for Surgery, Therapy, and Radiology)
- imec-Vision Lab (ASTRA Group)
- LEAP Project, Lawrence Livermore National Laboratory
- Pattern Recognition Lab (LME)
Topic
Paper
- A CT Geometry With Multiple Centers Of Rotation For Solving Sparse View Problem
- A novel digital tomosynthesis (DTS) reconstruction method using a deformation field map
- Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction
- An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of View
- Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT
- BaggageCT
- Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
- Cone-beam artifact reduction in Gamma Knife CBCT images using a line-arc-line scan trajectory
- DSigNet
- Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration
- Epi-NAF
- Exact-GS
- FedM2CT
- GB-SVFBP
- Geometry-Aware Diffusion Approximate Posterior Sampling for Sparse-View and Limited-Angle CT
- GGLF
- Gradient-Based Trajectory Optimisation over Continuous Poses for Sparse-View Cone-Beam CT
- Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models
- Model-Based Iterative Reconstruction with View-Dependent Detector Displacements for Cone-Beam CT
- Motion-Grad
- Prior-Adapted Progressive Time-Resolved CBCT Reconstruction Using a Dynamic Reconstruction and Motion Estimation Method
- Revisiting Pose Sensitivity in Splat-based Computed Tomography under Sparse-view Reconstruction
- Riner
- Robustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT under Challenging Acquisition Settings
- Texture-preserving implicit neural representation for cone-beam CT truncated reconstruction
- TG-Field
- Tomographic Sparse View Selection Using the View Covariance Loss
- TomoTransformer
- X-Splat