ArticleIEEE transactions on medical imaging2017
A Biomechanical Modeling Guided CBCT Estimation Technique.
Article in IEEE transactions on medical imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- DiffINR: conditional neural field diffusion for deformation-driven volumetric image estimation from limited measurements.Physics in medicine and biology · 2026Article
- A hybrid population-based and patient-specific framework for 2D-3D deformable registration-driven limited-angle cone-beam CT estimation.Medical physics · 2025Article
- A conditional point cloud diffusion model for deformable liver motion tracking via a single arbitrarily-angled x-ray projection.Physics in medicine and biology · 2025Article
- Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR).Physics in medicine and biology · 2024Article
- Deep Filtered Back Projection for CT Reconstruction.IEEE access : practical innovations, open solutions · 2024Article
- Real-time liver motion estimation via deep learning-based angle-agnostic X-ray imaging.Medical physics · 2023Article
- 2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking.Bioengineering (Basel, Switzerland) · 2023Article
- LiftReg: Limited Angle 2D/3D Deformable Registration.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio).Medical physics · 2021Article
- An unsupervised 2D-3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation.Physics in medicine and biology · 2021Article
- A statistical weighted sparse-based local lung motion modelling approach for model-driven lung biopsy.International journal of computer assisted radiology and surgery · 2020Article
- Finite element analysis of a one-piece zirconia implant in anterior single tooth implant applications.PloS one · 2020Article
- Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model.Quantitative imaging in medicine and surgery · 2019Article
- 4D liver tumor localization using cone-beam projections and a biomechanical model.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2019Article
- Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning.Visual computing for industry, biomedicine, and art · 2019Article
- Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation.Computers in biology and medicine · 2018Article
- A Novel method to generate on-board 4D MRI using prior 4D MRI and on-board kV projections from a conventional LINAC for target localization in liver SBRT.Medical physics · 2018Article
- Statistical Iterative CBCT Reconstruction Based on Neural Network.IEEE transactions on medical imaging · 2018Article
- A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction.Physics in medicine and biology · 2018Article
- Low-Dose CBCT Reconstruction Using Hessian Schatten Penalties.IEEE transactions on medical imaging · 2017Article
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Authors and funding
3 authors.
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Abstract
Two-dimensional-to-three-dimensional (2D-3D) deformation has emerged as a new technique to estimate cone-beam computed tomography (CBCT) images. The technique is based on deforming a prior high-quality 3D CT/CBCT image to form a new CBCT image, guided by limited-view 2D projections. The accuracy of this intensity-based technique, however, is often limited in low-contrast image regions with subtle intensity differences. The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures. Specifically, Bio-CBCT-est first extracts the 2D-3D deformation-generated displacement vectors at the high-contrast anatomical structure boundaries. The extracted surface deformation fields are subsequently used as the boundary conditions to drive structure-based FEA to correct and fine-tune the overall deformation fields, especially those at low-contrast regions within the structure. The resulting FEA-corrected deformation fields are then fed back into 2D-3D deformation to form an iterative loop, combining the benefits of intensity-based deformation and biomechanical modeling for CBCT estimation. Using eleven lung cancer patient cases, the accuracy of the Bio-CBCT-est technique has been compared to that of the 2D-3D deformation technique and the traditional CBCT reconstruction techniques. The accuracy was evaluated in the image domain, and also in the DVF domain through clinician-tracked lung landmarks.
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