Evidence map›Paper›PMID 37685285›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Faster Elbow MRI with Deep Learning Reconstruction-Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging.

Judith Herrmann, Saif Afat, Sebastian Gassenmaier, Jan-Peter Grunz, Gregor Koerzdoerfer, Andreas Lingg, Haidara Almansour, Dominik Nickel, Theresa Sophie Patzer, Sebastian Werner

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 28% 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

5 citing papers in PubMed, 5 citations in OpenAlex.

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  5. Review
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

10 authors at 3 institutions in 1 country.

Judith HerrmannDepartment of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany.ORCID 0000-0003-1535-8980
Saif AfatDepartment of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany.ORCID 0000-0002-6861-6245
Sebastian GassenmaierDepartment of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany.ORCID 0000-0003-3701-2227
Jan-Peter GrunzDepartment of Diagnostic and Interventional Radiology, University Hospital Würzburg, 97080 Würzburg, Germany.ORCID 0000-0002-4524-1620
Gregor KoerzdoerferMR Application Predevelopment, Siemens Healthcare GmbH, 91052 Erlangen, Germany.ORCID 0000-0002-8881-9517
Andreas LinggDepartment of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany.ORCID 0000-0001-8583-8627
Haidara AlmansourDepartment of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany.
Dominik NickelMR Application Predevelopment, Siemens Healthcare GmbH, 91052 Erlangen, Germany.ORCID 0000-0002-0360-7233
Theresa Sophie PatzerDepartment of Diagnostic and Interventional Radiology, University Hospital Würzburg, 97080 Würzburg, Germany.ORCID 0000-0001-6169-1552
Sebastian WernerDepartment of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany.
University Children's Hospital Tübingen · DESiemens Healthineers (Germany) · DEUniversitätsklinikum Würzburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the elbow regarding image quality and visualization of anatomy. MATERIALS AND

methodsBetween October 2020 and June 2021, seventeen participants (eight patients, nine healthy subjects; mean age: 43 ± 16 (20-70) years, eight men) were prospectively included in this study. Each patient underwent two examinations: standard MRI, including TSE sequences reconstructed with a generalized autocalibrating partial parallel acquisition reconstruction (TSE

resultsImage quality was significantly improved in TSE

conclusionTSE

Indexed as

accelerationdeep learning reconstructionelbowimage processingMRI

Identifiers

PMID37685285
PMCPMC10486923
OpenAlexW4386119752

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.