Evidence map›Paper›PMID 39139259›Full record

ArticlePolish journal of radiology2024

The utility of virtual monochromatic dual-energy computed tomography (DECT) in meniscal imaging: a technical evaluation.

Scott Adams, Jacques van Heerden, Jodie Reis, Prosanta Mondal, Michael Shepel, Haron Obaid

Abstract read
In one paragraph

Article in Polish journal of radiology, 2024. 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.

Scott AdamsUniversity of Saskatchewan, Canada.
Jacques van HeerdenUniversity of Saskatchewan, Canada.
Jodie ReisUniversity of Saskatchewan, Canada.
Prosanta MondalUniversity of Saskatchewan, Canada.
Michael ShepelUniversity of Saskatchewan, Canada.
Haron ObaidUniversity of Saskatchewan, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To determine the feasibility of meniscal imaging using virtual monochromatic images obtained through dualenergy computed tomography (DECT) technique, and to determine which keV levels optimise contrast resolution. Material and methods: All DECT exams were performed on a Discovery CT750 HD system from GE Healthcare. Virtual monochromatic images were reconstructed at 40 keV, 73 keV, 106 keV, and 139 keV. Contrast resolution of the medial and lateral menisci using a 5-point Likert scale at each keV level was determined through a consensus agreement by 2 fellowship-trained musculoskeletal radiologists. Friedman's and Wilcoxon signed rank tests were used to compare visualisation scores across different keV levels. Results: Seventeen knee exams from 10 patients met criteria for inclusion in the study. All patients included in the study cohort were male. The median age of patients was 46 years (interquartile range, 35-53 years). Virtual monochromatic images at 40 keV demonstrated highest contrast resolution of the menisci, with a statistically significant difference between contrast resolution scores at 40 keV and 76 keV, Friedman test: Conclusions: Meniscal imaging is feasible using DECT virtual monochromatic images at low keV levels. Improved contrast resolution at these specified KeV values could pave the way for further research in this field to determine its role in the future as an alternative option for assessment of the menisci in patients with contraindications to MRI or in the setting of a periarticular ferromagnetic foreign body obscuring the field of view.

Indexed as

contrast resolutiondual-energy CTmeniscus

Identifiers

PMID39139259
PMCPMC11321026

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Registered trials

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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.