Evidence map›Paper›PMID 33289152›Full record

ArticleJournal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine2020

An Automated ALARA Method for Ultrasound: An Obstetric Ultrasound Feasibility Study.

Katelyn Flint, Nick Bottenus, David Bradway, Patricia McNally, Sarah Ellestad, Gregg Trahey

Open access · greenAbstract read
In one paragraph

Article in Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.2field-weighted citation impact, top 19% 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

10 citing papers in PubMed, 17 citations in OpenAlex.

  1. Trial
  2. Article
  3. Review
  4. In Vivo Demonstration of a Real-Time Temporal SNR Acoustic Output Adjustment Method.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024
    Article
  5. Article
  6. An Automated Region-Selection Method for Adaptive ALARA Ultrasound Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2022
    Article
  7. Spatial Coherence in Medical Ultrasound: A Review.Ultrasound in medicine & biology · 2022
    Review
  8. Spatiotemporal Coherence to Quantify Sources of Image Degradation in Ultrasonic Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2022
    Article
  9. Article
  10. 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

6 authors at 2 institutions in 1 country.

Katelyn FlintDepartment of Biomedical Engineering, Duke University, Durham, North Carolina, USA.ORCID https://orcid.org/0000-0002-2048-0051
Nick BottenusDepartment of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
David BradwayDepartment of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
Patricia McNallyDepartment of Women's and Children's Services, Duke University Hospital, Durham, North Carolina, USA.
Sarah EllestadDivision of Maternal-Fetal Medicine, Duke University Medical Center, Durham, North Carolina, USA.
Gregg TraheyDepartment of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
Duke University · USDuke Children's Hospital & Health Center · US

Funding

Coherence-Based Fetal Ultrasonic ImagingR01EB017711 · NIBIB · DUKE UNIVERSITY · PI TRAHEY, GREGG E. · 2014 to 2025
$4.7M
A Patient-Adaptive, High MI Abdominal ScannerR01EB026574 · NIBIB · DUKE UNIVERSITY · PI TRAHEY, GREGG E. · 2018 to 2021
$2.3M
MODULATION OF SKELETAL INTERGRITY--DIETARY FAT WITH AGER03AG017711 · NIA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI CORWIN, REBECCA L · 1999 to 1999
–
Division of Graduate Education, National Science Foundation DGE1644868National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health R01EB017711National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health R01EB026574NIBIB NIH HHS R01 EB017711NIBIB NIH HHS R01 EB026574
6 · The paper itself

Abstract

objectivesUltrasound users are advised to observe the ALARA (as low as reasonably achievable) principle, but studies have shown that most do not monitor acoustic output metrics. We developed an adaptive ultrasound method that could suggest acoustic output levels based on real-time image quality feedback using lag-one coherence (LOC).

methodsLag-one coherence as a function of the mechanical index (MI) was assessed in 35 healthy volunteers in their second trimester of pregnancy. While imaging the placenta or the fetal abdomen, the system swept through 16 MI values ranging from 0.15 to 1.20. The LOC-versus-MI data were fit with a sigmoid curve, and the ALARA MI was selected as the point at which the fit reached 98% of its maximum.

resultsIn this study, the ALARA MI values were between 0.35 and 1.03, depending on the acoustic window. Compared to a default MI of 0.8, the pilot acquisitions suggested a lower ALARA MI 80% of the time. The contrast, contrast-to-noise ratio, generalized contrast-to-noise ratio, and LOC all followed sigmoidal trends with an increasing MI. The R

conclusionsThese results suggest that maximum image quality can be achieved with acoustic output levels lower than the US Food and Drug Administration limits in many cases, and an automated tool could be used in real time to find the ALARA MI for specific imaging conditions. Our results support the feasibility of an automated, LOC-based implementation of the ALARA principle for obstetric ultrasound.

Indexed as

acoustic exposureas low as reasonably achievablefetal ultrasoundimage qualitysafety

Identifiers

PMID33289152
PMCPMC10117178
OpenAlexW3110394202

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.