Evidence map›Paper›PMID 35092520›Full record

ArticleEJNMMI physics2022

Aortic valve imaging using

Martin Lyngby Lassen, Evangelos Tzolos, Daniele Massera, Sebastien Cadet, Rong Bing, Jacek Kwiecinski, Damini Dey, Daniel S Berman, Marc R Dweck, David E Newby and 1 more

Open access · goldAbstract read
In one paragraph

Article in EJNMMI physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 25% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Review
  3. Advanced Cardiac Imaging in the Assessment of Aortic Stenosis.Journal of cardiovascular development and disease · 2023
    Review
  4. Article
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

11 authors at 4 institutions in 4 countries.

Martin Lyngby LassenDepartment of Medicine (Division of Artificial Intelligence in Medicine), Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. Metro 203, Los Angeles, CA, 90048, USA.
Evangelos TzolosDepartment of Imaging, Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. Metro 203, Los Angeles, CA, 90048, USA.
Daniele MasseraLeon H. Charney Division of Cardiology, New York University School of Medicine, New York, NY, USA.
Sebastien CadetDepartment of Imaging, Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. Metro 203, Los Angeles, CA, 90048, USA.
Rong BingBritish Heart Foundation Centre for Cardiovascular Science, Clinical Research Imaging Centre, Edinburgh Heart Centre, University of Edinburgh, Edinburgh, UK.
Jacek KwiecinskiDepartment of Imaging, Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. Metro 203, Los Angeles, CA, 90048, USA.
Damini DeyDepartment of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. Metro 203, Los Angeles, CA, 90048, USA.
Daniel S BermanDepartment of Imaging, Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. Metro 203, Los Angeles, CA, 90048, USA.
Marc R DweckBritish Heart Foundation Centre for Cardiovascular Science, Clinical Research Imaging Centre, Edinburgh Heart Centre, University of Edinburgh, Edinburgh, UK.
David E NewbyBritish Heart Foundation Centre for Cardiovascular Science, Clinical Research Imaging Centre, Edinburgh Heart Centre, University of Edinburgh, Edinburgh, UK.
Piotr J SlomkaDepartment of Medicine (Division of Artificial Intelligence in Medicine), Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. Metro 203, Los Angeles, CA, 90048, USA. piotr.slomka@cshs.org.ORCID http://orcid.org/0000-0002-6110-938X
Cedars-Sinai Medical Center · USBritish Heart Foundation · GBNew York University · USUniversity of Copenhagen · DK

Funding

Integrated analysis of coronary anatomy and biology with 18F-fluoride PET and CT angiographyR01HL135557 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI SLOMKA, PIOTR J · 2017 to 2020
$3.0M
British Heart Foundation CH/09/002/26360British Heart Foundation FS/13/77/30488British Heart Foundation FS/14/78/31020British Heart Foundation FS/CRTF/20/24086British Heart Foundation PG/19/40/34422British Heart Foundation RG/16/10/32375Medical Research Council G0701127NHLBI NIH HHS R01 HL135557NHLBI NIH HHS R01HL135557
6 · The paper itself

Abstract

backgroundCurrent MATERIALS AND

methodsFourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUV

resultsCompared to standard and ECG-MC reconstructions, increases in both SUV

conclusion3 × MC improves the correlation between the AVCS and SUV

Indexed as

18F-sodium fluorideAortic valve imagingCardiac PETMotion correctionPET/CT

Identifiers

PMID35092520
PMCPMC8800969
OpenAlexW4210564909

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.