Evidence map›Paper›PMID 30235760›Full record

ArticleMedicine2018

Application of siemens SMART neuro attenuation correction in brain PET imaging.

Xiaonan Shao, Mei Xu, Chun Qiu, Rong Niu, Yuetao Wang, Xiaosong Wang

Abstract readEvaluation Study
In one paragraph

Article in Medicine, 2018. 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

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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

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5 · Who and what money

Authors and funding

6 authors.

Xiaonan ShaoDepartment of Nuclear Medicine, the third affiliated hospital of Soochow University, Changzhou, China.
Mei Xu
Chun Qiu
Rong Niu
Yuetao Wang
Xiaosong Wang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Siemens SMART neuro attenuation correction (SNAC) is a new type of calculated attenuation correction (CAC) method. This article aimed to evaluate the effect of SNAC on the quantitative analysis of brain positron emission tomography (PET) imaging.Brain PET images of 52 healthy participants after reconstructed by SNAC and CT attenuation correction (CTAC) were analyzed qualitatively by visual analysis, and quantitatively by Scenium software to compare their contrast, signal-to-noise ratio (SNR) as well as the mean standardized uptake value (SUVmean) of different brain regions.Compared with CTAC, reconstruction of images by SNAC significantly reduced the SNR by 17.3% (P < .001), but not affected the contrast (P = .440). In addition, the SUVmean of different brain regions in images reconstructed by SNAC is increased, but still significantly correlated with that by CTAC (r = 0.988, P < .001), with a coefficient of R = 0.976 in linear regression analysis. Moreover, the mean percent difference of SUVmean between images reconstructed with SNAC and CTAC was 8.03% ± 5.38%, varying significantly in the range of -7.56% to 75.31% among 10 different brain regions (F = 35.702, P < .001) and showed greater percent difference in the peripheral brain regions than in the mesial brain regions.Image reconstruction by SNAC has greater effect on quantitative analysis by increasing SUVmean of different brain regions to varying degrees, but has little influence on the brain PET image quality. Moreover, it simplifies examination process and reduces radiation dose, which is beneficial to pediatric patients as well as serial scans to monitor therapy.

Indexed as

AdultAgedAged, 80 and overBrainFemaleHealthy VolunteersHumansImage Processing, Computer-AssistedMaleMiddle AgedNeuroimagingPositron-Emission TomographySignal-To-Noise Ratio

Identifiers

PMID30235760
PMCPMC6160143

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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.