ArticlePhysics in medicine and biology2017
Correction of patient motion in cone-beam CT using 3D-2D registration.
Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Differentiable Forward and Back-Projector for Rigid Motion Estimation in X-Ray Imaging.IEEE transactions on bio-medical engineering · 2026Article
- [Motion parameter decoupling and motion constraint-driven optimization for correcting rigid motion artifacts in cone-beam computed tomography].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Diffusion Model-Based Motion Correction in Portable Computed Tomography for Brain: A Human Observer Study.Academic radiology · 2026Article
- Joint Cone-Beam CT Reconstruction and Rigid Motion Compensation Using A Differentiable Projector.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Deep learning-based cone-beam CT motion compensation with single-view temporal resolution.Medical physics · 2025Article
- [A segmented backprojection tensor degradation feature encoding model for motion artifacts correction in dental cone beam computed tomography].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Head CT Scan Motion Artifact Correction via Diffusion-Based Generative Models.Applications of medical artificial intelligence : third international workshop, AMAI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024 : proceedings. AMAI (Workshop) (3rd : 2024 : Marrakech, Morocco) · 2025Article
- Synthetic CT generation from CBCT using deep learning for adaptive radiotherapy in prostate cancer.Frontiers in radiology · 2025Article
- Portable head CT motion artifact correction via diffusion-based generative model.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Vessel-targeted compensation of deformable motion in interventional cone-beam CT.Medical image analysis · 2024Article
- Optimizing Conditional DDPM for Head CT Motion Artifact Reduction: Brain vs. Skull and 3D vs. 2D.Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography · 2024Article
- [A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Deformable motion compensation in interventional cone-beam CT with a context-aware learned autofocus metric.Medical physics · 2024Article
- Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.Medical physics · 2024Article
- Evaluation of Motion Artifact Correction Technique for Cone-Beam Computed Tomography Image Considering Blood Vessel Geometry.Journal of clinical medicine · 2024Article
- Dual-Layer Detector Cone-Beam CT Angiography for Stroke Assessment: First-in-Human Results (the Next Generation X-ray Imaging System Trial).AJNR. American journal of neuroradiology · 2023Article
- Reference-free learning-based similarity metric for motion compensation in cone-beam CT.Physics in medicine and biology · 2022Article
- An unsupervised 2D-3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation.Physics in medicine and biology · 2021Article
- [Four-dimensional cone-beam CT reconstruction based on motion-compensated robust principal component analysis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- Deformable motion compensation for interventional cone-beam CT.Physics in medicine and biology · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
Cone-beam CT (CBCT) is increasingly common in guidance of interventional procedures, but can be subject to artifacts arising from patient motion during fairly long (~5-60 s) scan times. We present a fiducial-free method to mitigate motion artifacts using 3D-2D image registration that simultaneously corrects residual errors in the intrinsic and extrinsic parameters of geometric calibration. The 3D-2D registration process registers each projection to a prior 3D image by maximizing gradient orientation using the covariance matrix adaptation-evolution strategy optimizer. The resulting rigid transforms are applied to the system projection matrices, and a 3D image is reconstructed via model-based iterative reconstruction. Phantom experiments were conducted using a Zeego robotic C-arm to image a head phantom undergoing 5-15 cm translations and 5-15° rotations. To further test the algorithm, clinical images were acquired with a CBCT head scanner in which long scan times were susceptible to significant patient motion. CBCT images were reconstructed using a penalized likelihood objective function. For phantom studies the structural similarity (SSIM) between motion-free and motion-corrected images was >0.995, with significant improvement (p < 0.001) compared to the SSIM values of uncorrected images. Additionally, motion-corrected images exhibited a point-spread function with full-width at half maximum comparable to that of the motion-free reference image. Qualitative comparison of the motion-corrupted and motion-corrected clinical images demonstrated a significant improvement in image quality after motion correction. This indicates that the 3D-2D registration method could provide a useful approach to motion artifact correction under assumptions of local rigidity, as in the head, pelvis, and extremities. The method is highly parallelizable, and the automatic correction of residual geometric calibration errors provides added benefit that could be valuable in routine use.
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Registered trials
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