ArticlePhysics in medicine and biology2021
An unsupervised 2D-3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation.
Article in Physics in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- DiffINR: conditional neural field diffusion for deformation-driven volumetric image estimation from limited measurements.Physics in medicine and biology · 2026Article
- PolyPose: Deformable 2D/3D Registration via Polyrigid Transformations.Advances in neural information processing systems · 2025Article
- A hybrid population-based and patient-specific framework for 2D-3D deformable registration-driven limited-angle cone-beam CT estimation.Medical physics · 2025Article
- Article
- 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
- MUsculo-Skeleton-Aware (MUSA) deep learning for anatomically guided head-and-neck CT deformable registration.Medical image analysis · 2025Article
- Non-coplanar CBCT image reconstruction using a generative adversarial network for non-coplanar radiotherapy.Journal of applied clinical medical physics · 2024Article
- Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction.Physics in medicine and biology · 2024Article
- A review of the clinical introduction of 4D particle therapy research concepts.Physics and imaging in radiation oncology · 2024Review
- Real-time liver motion estimation via deep learning-based angle-agnostic X-ray imaging.Medical physics · 2023Article
- Unsupervised deep learning registration model for multimodal brain images.Journal of applied clinical medical physics · 2023Article
- 2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking.Bioengineering (Basel, Switzerland) · 2023Article
- Real-time MRI motion estimation through an unsupervised k-space-driven deformable registration network (KS-RegNet).Physics in medicine and biology · 2022Article
- 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
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Abstract
Acquiring CBCTs from a limited scan angle can help to reduce the imaging time, save the imaging dose, and allow continuous target localizations through arc-based treatments with high temporal resolution. However, insufficient scan angle sampling leads to severe distortions and artifacts in the reconstructed CBCT images, limiting their clinical applicability. 2D-3D deformable registration can map a prior fully-sampled CT/CBCT volume to estimate a new CBCT, based on limited-angle on-board cone-beam projections. The resulting CBCT images estimated by 2D-3D deformable registration can successfully suppress the distortions and artifacts, and reflect up-to-date patient anatomy. However, traditional iterative 2D-3D deformable registration algorithm is very computationally expensive and time-consuming, which takes hours to generate a high quality deformation vector field (DVF) and the CBCT. In this work, we developed an unsupervised, end-to-end, 2D-3D deformable registration framework using convolutional neural networks (2D3D-RegNet) to address the speed bottleneck of the conventional iterative 2D-3D deformable registration algorithm. The 2D3D-RegNet was able to solve the DVFs within 5 seconds for 90 orthogonally-arranged projections covering a combined 90° scan angle, with DVF accuracy superior to 3D-3D deformable registration, and on par with the conventional 2D-3D deformable registration algorithm. We also performed a preliminary robustness analysis of 2D3D-RegNet towards projection angular sampling frequency variations, as well as scan angle offsets. The synergy of 2D3D-RegNet with biomechanical modeling was also evaluated, and demonstrated that 2D3D-RegNet can function as a fast DVF solution core for further DVF refinement.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.