Evidence map›Paper›PMID 40674608›Full record

ArticleMagnetic resonance in medicine2025

Automatic selection of optimal TI for flow-independent dark-blood delayed-enhancement MRI.

Andreea Bianca Popescu, Wolfgang Rehwald, David Wendell, Céleste Chevalier, Lucian Mihai Itu, Constantin Suciu, Teodora Chitiboi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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.

Andreea Bianca PopescuDepartment of Automation and Information Technology, Transilvania University of Brasov, Brasov, Romania.ORCID https://orcid.org/0009-0001-2276-4728
Wolfgang RehwaldSiemens Medical Solutions USA, Inc., Malvern, Pennsylvania, USA.
David WendellDuke Cardiovascular MR Center, Duke University, Durham, North Carolina, USA.
Céleste ChevalierDuke Cardiovascular MR Center, Duke University, Durham, North Carolina, USA.
Lucian Mihai ItuDepartment of Automation and Information Technology, Transilvania University of Brasov, Brasov, Romania.
Constantin SuciuDepartment of Automation and Information Technology, Transilvania University of Brasov, Brasov, Romania.
Teodora ChitiboiSiemens Healthineers AG, Hamburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePropose and evaluate an automatic approach for predicting the optimal inversion time (TI) for dark and gray blood images for flow-independent dark-blood delayed-enhancement (FIDDLE) acquisition based on free-breathing FIDDLE TI-scout images.

methodsIn 267 patients, the TI-scout sequence acquired single-shot magnetization-prepared and associated reference images (without preparation) on a 3 T Magnetom Vida and a 1.5 T Magnetom Sola scanner. Data were reconstructed into phase-corrected TI-scout images typically covering TIs from 140 to 440 ms (20 ms increment). A deep learning network was trained to segment the myocardium and blood pool in reference images. These segmentation masks were transferred to the TI-scout images to derive intensity features of myocardium and blood, with which T

resultsThe pipeline predicted optimal TIs with an average error less than 10 ms compared to manually annotated optimal TIs.

conclusionThe presented approach reliably and automatically predicted optimal TI for dark and gray blood FIDDLE acquisition, with an average error less than the TI increment of the FIDDLE TI-scout sequence.

Indexed as

HeartImage EnhancementImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMagnetic Resonance ImagingAdultAgedAlgorithmsDeep LearningFemaleHumansMaleMiddle AgedMyocardiumautomatic TIdeep learningFIDDLELGEmachine learningoptimal TI

Identifiers

PMID40674608
PMCPMC12501742

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.