ArticlePhysics in medicine and biology2021
Deformable motion compensation for interventional cone-beam CT.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Differentiable Forward and Back-Projector for Rigid Motion Estimation in X-Ray Imaging.IEEE transactions on bio-medical engineering · 2026Article
- Forward and Back Projectors for Gradient-based Rigid Motion Estimation in X-ray Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Motion correction in CBCT imaging using gate-less model-based reconstruction of non-rigid motion and images.Medical physics · 2025Article
- Deep learning-based cone-beam CT motion compensation with single-view temporal resolution.Medical physics · 2025Article
- Vessel-targeted compensation of deformable motion in interventional cone-beam CT.Medical image analysis · 2024Article
- Contrast-enhanced photon-counting micro-CT of tumor xenograft models.Physics in medicine and biology · 2024Article
- Deformable motion compensation in interventional cone-beam CT with a context-aware learned autofocus metric.Medical physics · 2024Article
- Contrast-enhanced photon-counting micro-CT of tumor xenograft models.bioRxiv : the preprint server for biology · 2024Article
- Deformable Motion Compensation for Intraprocedural Vascular Cone-beam CT with Sequential Projection Domain Targeting and Vessel-Enhancing Autofocus.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Reference-free learning-based similarity metric for motion compensation in cone-beam CT.Physics in medicine and biology · 2022Article
- Simulation of Random Deformable Motion in Soft-Tissue Cone-Beam CT with Learned Models.Proceedings of SPIE--the International Society for Optical Engineering · 2022Article
- Context-Aware, Reference-Free Local Motion Metric for CBCT Deformable Motion Compensation.Proceedings of SPIE--the International Society for Optical Engineering · 2022Article
- "One Shot" Sample Evaluation of 22G, 22G upgraded, 21G and 19G needle for Endobronchial Ultrasound-EBUS-TBNA.Journal of Cancer · 2022Article
- Deformable motion compensation for interventional cone-beam CT.Physics in medicine and biology · 2021Article
- Targeted Deformable Motion Compensation for Vascular Interventional Cone-Beam CT Imaging.Proceedings of SPIE--the International Society for Optical EngineeringArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5-30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%-92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5-5 mm
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.