Evidence map›Paper›PMID 38388717›Full record

ArticleEuropean radiology2024

Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM).

Alexa E Golbus, Chloe Steveson, John L Schuzer, Shirley F Rollison, Tat'Yana Worthy, Amanda M Jones, Patricia Julien-Williams, Joel Moss, Marcus Y Chen

Open access · hybridAbstract read
In one paragraph

Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 6% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Brazilian Thoracic Association recommendations for the management of lymphangioleiomyomatosis.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2025
    Guideline
  2. Towards optimized CT lung cancer screening scan protocols.The British journal of radiology · 2026
    Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

Alexa E GolbusCardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Dr, MSC 1046, Building 10, Room B1D47, Bethesda, MD, 20892, USA.
Chloe StevesonCanon Medical Systems, Otawara, Japan.
John L SchuzerCanon Medical Systems, Otawara, Japan.
Shirley F RollisonRadiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA.
Tat'Yana WorthyOffice of the Clinical Director, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, USA.
Amanda M JonesCritical Care Medicine and Pulmonary Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, USA.
Patricia Julien-WilliamsCritical Care Medicine and Pulmonary Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, USA.
Joel MossCritical Care Medicine and Pulmonary Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, USA.
Marcus Y ChenCardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Dr, MSC 1046, Building 10, Room B1D47, Bethesda, MD, 20892, USA. marcus.chen@nih.gov.ORCID http://orcid.org/0000-0003-0743-9369
National Institutes of Health · USCanon (Japan) · JPNational Institutes of Health Clinical Center · US

Funding

Characterization of the Pathogenesis of Lymphangioleiomyomatosis (LAM)ZIAHL002541 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI MOSS, JOEL · 2009 to 2025
$47.7M
Cardiovascular CT Clinical ResearchZIAHL006220 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI CHEN, MARCUS · 2017 to 2025
$22.5M
Intramural NIH HHS ZIA HL002541NHLBI Division of Intramural Research 1ZIAHL002541NHLBI Division of Intramural Research 1ZIAHL006220
6 · The paper itself

Abstract

purposeFrequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use of SilverBeam filter and deep learning reconstruction algorithm allows for reduced radiation dose chest CT scanning in patients with lymphangioleiomyomatosis (LAM). MATERIAL AND

methodsIn a single-center prospective study, 60 consecutive patients with LAM underwent chest CT at standard and ultra-low radiation doses. Standard dose scan was performed with standard copper filter and ultra-low dose scan was performed with SilverBeam filter. Scans were reconstructed using a soft tissue kernel with deep learning reconstruction (AiCE) technique and using a soft tissue kernel with hybrid iterative reconstruction (AIDR3D). Cyst scores were quantified by semi-automated software. Signal-to-noise ratio (SNR) was calculated for each reconstruction. Data were analyzed by linear correlation, paired t-test, and Bland-Altman plots.

resultsPatients averaged 49.4 years and 100% were female with mean BMI 26.6 ± 6.1 kg/m

conclusionSilverBeam filter with deep learning reconstruction reduces radiation dosage of chest CT, while maintaining accuracy of cyst quantification as well as image quality in cystic lung disease. CLINICAL RELEVANCE STATEMENT: Radiation dosage from chest CT can be significantly reduced without sacrificing image quality by using silver filter in combination with a deep learning reconstructive algorithm. KEY POINTS: • Deep learning reconstruction in chest CT had no significant effect on cyst quantification when compared to conventional hybrid iterative reconstruction. • SilverBeam filter reduced radiation dosage by 85.5% compared to standard dose chest CT. • SilverBeam filter in coordination with deep learning reconstruction maintained image quality and diagnostic accuracy for cyst quantification when compared to standard dose CT with hybrid iterative reconstruction.

Indexed as

Deep LearningLymphangioleiomyomatosisRadiation DosageSilverTomography, X-Ray ComputedAdultAlgorithmsFemaleHumansLung NeoplasmsMaleMiddle AgedProspective StudiesRadiographic Image Interpretation, Computer-AssistedRadiography, ThoracicSilverDeep learningLung diseasesRadiation dosageTomography (X-ray computed)

Identifiers

PMID38388717
PMCPMC11364713
OpenAlexW4392024288

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.