ArticlePhysics in medicine and biology2020
C-arm orbits for metal artifact avoidance (MAA) in cone-beam CT.
Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Parametric-MAA: fast, object-centric avoidance of metal artifacts for intraoperative CBCT.International journal of computer assisted radiology and surgery · 2025Article
- Source-detector trajectory optimization for CBCT metal artifact reduction based on PICCS reconstruction.Zeitschrift fur medizinische Physik · 2024Article
- Universal non-circular cone beam CT orbits for metal artifact reduction imaging during image-guided procedures.Scientific reports · 2024Article
- Spectral Orbits: Combining Spectral Imaging and Non-Circular Orbits for Interventional CBCT.Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography · 2024Article
- Cone-beam CT with a noncircular (sine-on-sphere) orbit: imaging performance of a clinical system for image-guided interventions.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Fully automatic online geometric calibration for non-circular cone-beam CT orbits using fiducials with unknown placement.Medical physics · 2024Article
- Purse-string suture with nylon cords and metal clips for the treatment of duodenal fistulae under the endoscope: a case report.Frontiers in medicine · 2024Article
- Effect of changing the acquisition trajectory of the 3D C-arm (CBCT) on image quality in spine surgery: experimental study using an artificial bone model.Journal of orthopaedic surgery and research · 2023Article
- Cone-beam CT sampling incompleteness: analytical and empirical studies of emerging systems and source-detector orbits.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Accuracy Assessment of Percutaneous Pedicle Screw Placement Using Cone Beam Computed Tomography with Metal Artifact Reduction.Sensors (Basel, Switzerland) · 2022Article
- Universal orbit design for metal artifact elimination.Physics in medicine and biology · 2022Article
- Sinogram + image domain neural network approach for metal artifact reduction in low-dose cone-beam computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Metal artifacts present a challenge to cone-beam CT (CBCT) image-guided surgery, obscuring visualization of metal instruments and adjacent anatomy-often in the very region of interest pertinent to the imaging/surgical tasks. We present a method to reduce the influence of metal artifacts by prospectively defining an image acquisition protocol-viz., the C-arm source-detector orbit-that mitigates metal-induced biases in the projection data. The metal artifact avoidance (MAA) method is compatible with simple mobile C-arms, does not require exact prior information on the patient or metal implants, and is consistent with 3D filtered backprojection (FBP), more advanced (e.g. polyenergetic) model-based image reconstruction (MBIR), and metal artifact reduction (MAR) post-processing methods. The MAA method consists of: (i) coarse localization of metal objects in the field-of-view (FOV) via two or more low-dose scout projection views and segmentation (e.g. a simple U-Net) in coarse backprojection; (ii) model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices accessible by the imaging system (e.g. gantry rotation and tilt angles); and (iii) identification of a circular or non-circular orbit that reduces the variation in spectral shift. The method was developed, tested, and evaluated in a series of studies presenting increasing levels of complexity and realism, including digital simulations, phantom experiment, and cadaver experiment in the context of image-guided spine surgery (pedicle screw implants). The MAA method accurately predicted tilted circular and non-circular orbits that reduced the magnitude of metal artifacts in CBCT reconstructions. Realistic distributions of metal instrumentation were successfully localized (0.71 median Dice coefficient) from 2-6 low-dose scout views even in complex anatomical scenes. The MAA-predicted tilted circular orbits reduced root-mean-square error (RMSE) in 3D image reconstructions by 46%-70% and 'blooming' artifacts (apparent width of the screw shaft) by 20-45%. Non-circular orbits defined by MAA achieved a further ∼46% reduction in RMSE compared to the best (tilted) circular orbit. The MAA method presents a practical means to predict C-arm orbits that minimize spectral bias from metal instrumentation. Resulting orbits-either simple tilted circular orbits or more complex non-circular orbits that can be executed with a motorized multi-axis C-arm-exhibited substantial reduction of metal artifacts in raw CBCT reconstructions by virtue of higher fidelity projection data, which are in turn compatible with subsequent MAR post-processing and/or polyenergetic MBIR to further reduce artifacts.
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.