ArticleAJNR. American journal of neuroradiology2020
Diagnostic Value of Model-Based Iterative Reconstruction Combined with a Metal Artifact Reduction Algorithm during CT of the Oral Cavity.
Article in AJNR. American journal of neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- HyperSight CBCT dose calculation accuracy for adaptive head and neck radiotherapy: Results from a prospective clinical trial.Medical physics · 2026Trial
- HyperSight CBCT image quality and metal artifact reduction for adaptive head and neck radiotherapy: Results from a prospective clinical trial.Journal of applied clinical medical physics · 2025Trial
- Image-domain metal artifact reduction in low-energy VMI using high-energy regional prior.Medical physics · 2025Article
- Evaluation of the effect of reducing metal artifacts in multi-detector CT imaging of zirconia and titanium implants.Oral radiology · 2025Article
- Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction-Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT.Diagnostics (Basel, Switzerland) · 2023Article
- A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy.Visual computing for industry, biomedicine, and art · 2022Article
- Reconstruction Algorithm-Based CT Imaging for the Diagnosis of Hepatic Ascites.Computational and mathematical methods in medicine · 2022Article
- CT Image Features Based on the Reconstruction Algorithm for Continuous Blood Purification Combined with Nursing Intervention in the Treatment of Severe Acute Pancreatitis.Contrast media & molecular imaging · 2022Article
- Evaluation of the Treatment Planning and Delivery for Hip Implant Cases on Tomotherapy.Journal of medical physicsArticle
- Optimizing Positron Emission Tomography-Computed Tomography Image Quality with Iterative Reconstruction: A NEMA Phantom Study.Journal of medical physicsArticle
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeMetal artifacts reduce the quality of CT images and increase the difficulty of interpretation. This study compared the ability of model-based iterative reconstruction and hybrid iterative reconstruction to improve CT image quality in patients with metallic dental artifacts when both techniques were combined with a metal artifact reduction algorithm. MATERIALS AND
methodsThis retrospective clinical study included 40 patients (men, 31; women, 9; mean age, 62.9 ± 12.3 years) with oral and oropharyngeal cancer who had metallic dental fillings or implants and underwent contrast-enhanced ultra-high-resolution CT of the neck. Axial CT images were reconstructed using hybrid iterative reconstruction and model-based iterative reconstruction, and the metal artifact reduction algorithm was applied to all images. Finally, hybrid iterative reconstruction + metal artifact reduction algorithms and model-based iterative reconstruction + metal artifact reduction algorithm data were obtained. In the quantitative analysis, SDs were measured in ROIs over the apex of the tongue (metal artifacts) and nuchal muscle (no metal artifacts) and were used to calculate the metal artifact indexes. In a qualitative analysis, 3 radiologists blinded to the patients' conditions assessed the image-quality scores of metal artifact reduction and structural depictions.
resultsHybrid iterative reconstruction + metal artifact reduction algorithms and model-based iterative reconstruction + metal artifact reduction algorithms yielded significantly different metal artifact indexes of 82.2 and 73.6, respectively (95% CI, 2.6-14.7;
conclusionsModel-based iterative reconstruction + metal artifact reduction algorithms significantly reduced the artifacts and improved the image quality of structural depictions on neck CT images.
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