Evidence map›Paper›PMID 42565288›Full record

ArticleJournal of magnetic resonance imaging : JMRI2026

Autonomous Velocity Encoding (VENC) Selection Improves Precision of Quantitative Flow Measurement.

Pierre Daudé, Rajiv Ramasawmy, Christine Mancini, Dominique Franson, Sunggun Lee, Anastasia Tsakirellis, W Patricia Bandettini, Ahsan Javed, Kelvin Chow, Adrienne E Campbell-Washburn

Abstract read
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Pierre DaudéLaboratory of Imaging Technology, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung & Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0002-0565-8974
Rajiv RamasawmyLaboratory of Imaging Technology, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung & Blood Institute, NIH, Bethesda, Maryland, USA.
Christine ManciniCardiovascular Branch, Division of Intramural Research, National Heart, Lung & Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0009-0001-2852-4629
Dominique FransonSiemens Medical Solutions USA Inc, Malvern, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-9870-3147
Sunggun LeeLaboratory of Imaging Technology, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung & Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0009-0009-9025-7240
Anastasia TsakirellisLaboratory of Imaging Technology, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung & Blood Institute, NIH, Bethesda, Maryland, USA.
W Patricia BandettiniCardiovascular Branch, Division of Intramural Research, National Heart, Lung & Blood Institute, NIH, Bethesda, Maryland, USA.
Ahsan JavedLaboratory of Imaging Technology, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung & Blood Institute, NIH, Bethesda, Maryland, USA.
Kelvin ChowSiemens Healthcare Ltd, Calgary, Alberta, Canada.
Adrienne E Campbell-WashburnLaboratory of Imaging Technology, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung & Blood Institute, NIH, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0002-7169-5693

Funding

NHLBI NIH HHS Z01-HL006213NHLBI NIH HHS Z01-HL006257
6 · The paper itself

Abstract

backgroundThe optimal velocity encoding limit (VENC) in phase contrast MRI is subject-specific because it depends on peak flow rate and the presence of flow jets. Currently, VENC is set manually with limited prior knowledge of the peak velocity. Setting the correct VENC might improve measurement precision or shorten scan times. PURPOSE: To evaluate a workflow for autonomous selection of the optimal VENC without technologist interaction. STUDY TYPE: Prospective and retrospective. POPULATION: The retrospective cohort included 254 scans from 113 patients (47 ± 14 years, 58 female) and 18 healthy volunteers (34 ± 15 years, 10 female). The prospective cohort included 5 patients with abnormal flow (47 ± 19 years, 2 female) and 10 healthy volunteers (28 ± 7 years, 6 female). FIELD STRENGTH/SEQUENCE: 1.5 T, 0.55 T; Cartesian gradient echo phase-contrast sequence. ASSESSMENT: A workflow has been designed to autonomously estimate and prescribe the optimal VENC. A 30s free-breathing calibration scan was followed by automatic estimation of the maximum velocity (Vmax) in < 6 s. The calculated optimal VENC was then automatically applied in the subsequent flow measurement without operator intervention. The target VENC:Vmax ratio was 1.1-1.25 according to consensus statements. STATISTICAL TESTS: Shapiro-Wilk test, nonparametric two-sided bootstrap with 90% confidence intervals, Wilcoxon signed-rank test, paired t-test. Holm correction was applied to prespecified pairwise comparisons. p < 0.05 was considered significant.

resultsRetrospective analysis demonstrated sub-optimal VENC setting in 96.5% of examinations with a VENC:Vmax ratio [2.03, 2.13] (two-sided bootstrap 90% confidence interval). In the prospective cohort, autonomous inline VENC selection yielded a mean VENC:Vmax ratio of 1.19 ± 0.10, significantly lower than subject-invariant VENC settings (VENC DATA

conclusionInline autonomous VENC selection improved flow-measurement precision, simplified the acquisition workflow, and reduced scan time at 0.55 T. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: 1.

Indexed as

Image Processing, Computer-AssistedMagnetic Resonance ImagingAdultAlgorithmsBlood Flow VelocityCalibrationFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedProspective StudiesReproducibility of ResultsRetrospective StudiesWorkflowinline processingphase contrast flowvelocity encoding limit

Identifiers

PMID42565288
PMCPMC13667328

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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.