Evidence map›Paper›PMID 37406744›Full record

ArticleMagnetic resonance imaging2023

Combining neural networks and image synthesis to enable automatic thoracic cavity segmentation of hyperpolarized

Suphachart Leewiwatwong, Junlan Lu, Isabelle Dummer, Kevin Yarnall, David Mummy, Ziyi Wang, Bastiaan Driehuys

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance imaging, 2023. 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
1.2field-weighted citation impact, top 22% 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. Article
  3. Combining hyperpolarizedJournal of applied physiology (Bethesda, Md. : 1985) · 2025
    Article
  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

7 authors at 3 institutions in 2 countries.

Suphachart LeewiwatwongCenter for In Vivo Microscopy, Duke University Medical Center, Durham, NC, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Junlan LuCenter for In Vivo Microscopy, Duke University Medical Center, Durham, NC, USA; Department of Medical Physics, Duke University, Durham, NC, USA.
Isabelle DummerDepartment of Biomedical Engineering, McGill University, Montréal, QC, Canada.
Kevin YarnallDepartment of Mechanical Engineering, Duke University, Durham, NC, USA.
David MummyCenter for In Vivo Microscopy, Duke University Medical Center, Durham, NC, USA; Department of Radiology, Duke University Medical Center, Durham, NC.
Ziyi WangCenter for In Vivo Microscopy, Duke University Medical Center, Durham, NC, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Bastiaan DriehuysCenter for In Vivo Microscopy, Duke University Medical Center, Durham, NC, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Medical Physics, Duke University, Durham, NC, USA; Department of Radiology, Duke University Medical Center, Durham, NC,. Electronic address: bastiaan.driehuys@duke.edu.
Duke University · USDuke Medical Center · USMcGill University · CA

Funding

Early Detection of Changes in Pulmonary Gas Exchange by Hyperpolarized Xe MRIR01HL105643 · NHLBI · DUKE UNIVERSITY · PI Bastiaan Driehuys · 2011 to 2026
$6.7M
Using MRI To Visualize Regional Therapy Response In Idiopathic Pulmonary FibrosisR01HL126771 · NHLBI · DUKE UNIVERSITY · PI Bastiaan Driehuys · 2015 to 2026
$6.6M
Oxidative Stress and Regional Airway Remodeling and Fibrosis in Obese AsthmaR01HL153641 · NHLBI · DUKE UNIVERSITY · PI QUE, LORETTA G · 2020 to 2023
$3.0M
Xenon MRI in Pulmonary HypertensionR01HL153872 · NHLBI · DUKE UNIVERSITY · PI DRIEHUYS, BASTIAAN, RAJAGOPAL, SUDARSHAN K · 2021 to 2024
$2.9M
NHLBI NIH HHS R01 HL105643NHLBI NIH HHS R01 HL126771NHLBI NIH HHS R01 HL153641NHLBI NIH HHS R01 HL153872
6 · The paper itself

Abstract

RATIONALE AND

objectivesQuantification of MATERIALS AND

methodsTraining and testing data consisted of 22 and 33 3D

resultsPerformance of both U-net and 3D segmentation was improved by including synthetic training data. The 3D models performed significantly better than U-net, and the 3D model trained with synthetic

conclusionIt is feasible to obtain high-quality segmentations from

Indexed as

ProtonsThoracic CavityImage Processing, Computer-AssistedLungMagnetic Resonance ImagingNeural Networks, ComputerProtonsGenerative adversarial networksHyperpolarized gas imagingNeural networksU-netV-net

Identifiers

PMID37406744
PMCPMC10528669
OpenAlexW4382929847

What OpenQuestion holds

Textmetadata
LicenceTDM
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.