Evidence map›Paper›PMID 30259068›Full record

ArticlePediatric radiology2019

Extent of tube-current reduction using sinogram affirmed iterative reconstruction in pediatric computed tomography: phantom study.

Ajit Brindhaban

Abstract read
PubMed Publisher
In one paragraph

Article in Pediatric radiology, 2019. 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

1 author.

Ajit BrindhabanDepartment of Radiologic Sciences, Kuwait University, PO Box 31470, Sulaibikhat, 90805, Kuwait. ajit@hsc.edu.kw.ORCID http://orcid.org/0000-0002-0185-8151

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIterative image reconstruction techniques can produce diagnostic-quality computed tomography (CT) images with lower radiation dose.

objectiveTo quantify the reduction in x-ray tube-current setting and optimize pediatric CT scans using different strengths of an iterative reconstruction technique. MATERIALS AND

methodsThe head, chest and abdomen regions of an anthropomorphic phantom representing a 5-year-old patient were scanned using standard CT protocols. Images were reconstructed using filtered back projection and different strengths of a sinogram affirmed iterative reconstruction technique. Repeated measurements of contrast-to-noise ratios in the lungs, bone and soft-tissue regions of the phantom were carried out. Maximum increase in contrast-to-noise ratio with iterative reconstruction strength was identified and a tube-current reduction factor was calculated. Head scans were repeated with reduced tube current and compared to filtered back projection images.

resultsIterative reconstruction strength of 3 for head and chest images and 4 for abdomen images were optimum, resulting in contrast-to-noise ratio increase of about 50%. A tube-current reduction factor of 1.2 for head images was calculated. Images of the head acquired using reduced tube-current showed similar contrast-to-noise ratio as images form filtered back projection with full tube current.

conclusionOptimum strength of iterative reconstruction technique has been identified for head, chest and abdomen images. Reductions in tube current of 20%, resulting in similar radiation dose reduction, have been established.

Indexed as

Tomography, X-Ray ComputedChild, PreschoolHumansPhantoms, ImagingRadiation DosageRadiographic Image Interpretation, Computer-AssistedChildrenComputed tomographyContrast-to-noise ratioIterative reconstruction techniqueTube-current reduction

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.