Evidence map›Paper›PMID 41536889›Full record

ArticleImaging science in dentistry2025

Noise-optimized cone-beam computed tomographic imaging of different post materials with multiple settings: A comparative analysis.

Solaleh Shahmirzadi, Dilek Helvacioglu-Yigit, Umut Seki, Maharaj Singh, Sarang Saadat, Husniye Demirturk

Abstract read
In one paragraph

Article in Imaging science in dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Solaleh ShahmirzadiDivision of Oral and Maxillofacial Radiology, Department of Diagnostic Sciences, Texas A&M School of Dentistry, Dallas, USA.ORCID https://orcid.org/0000-0002-5276-3021
Dilek Helvacioglu-YigitCollege of Dental Medicine, QU Health, Qatar University, Doha, Qatar.ORCID https://orcid.org/0000-0001-5999-9726
Umut SekiDepartment of Dentomaxillofacial Radiology, Faculty of Dentistry, Kocaeli University, Kocaeli, Türkiye.ORCID https://orcid.org/0000-0002-0286-9792
Maharaj SinghCollege of Nursing, Marquette University, Milwaukee, USA.ORCID https://orcid.org/0000-0001-7736-1291
Sarang SaadatDepartment of Periodontics, Virginia Commonwealth University School of Dentistry, Richmond, USA.ORCID https://orcid.org/0000-0001-7705-3467
Husniye DemirturkBeamReaders Inc, Kennewick, USA.ORCID https://orcid.org/0000-0002-4808-0949

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The aim of this study was to evaluate the effect of 4 different post materials on the contrast-to-noise ratio (CNR) in various tooth regions using multiple cone-beam computed tomography (CBCT) imaging settings and metal artifact reduction (MAR) algorithms. Materials and Methods: Forty single-rooted teeth with 4 different post materials were mounted in a skull and scanned using the Planmeca ProMax 3D Max under different conditions: 90 and 96 kVp; no, medium, and high MAR settings; and voxel sizes of 100 and 200 μm. Image analysis was performed with ImageJ software to calculate CNR. The dose-area product (DAP) was recorded, and the effective dose was calculated. Results: When all other parameters were held constant, each factor significantly influenced mean CNR ( Conclusion: The combination of low resolution, medium MAR, and 96 kVp provided the highest image quality and CNR for visualizing teeth with post materials using the Planmeca ProMax, while also producing one of the lowest effective doses.

Indexed as

Cone-Beam Computed TomographyDental MaterialsSignal-To-Noise Ratio

Identifiers

PMID41536889
PMCPMC12798319

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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.