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Aortic valve imaging using cardiac magnetic resonance including T1 mapping in the assessment of severe aortic stenosis

2025· article· en· W7127949497 on OpenAlexaboutno aff
Henry Procter, Marilena Giannoudi, Sindhoora Kotha, Nicholas Jex, Carl Simela, Lizette Cash, D Beech, J Greenwood, D. Plein, M R Dweck, P Kellman, Eylem Levelt

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsStenosisMagnetic resonance imagingCardiac magnetic resonance imagingCalcificationAortic valve stenosisCardiac magnetic resonanceAortic valve

Abstract

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Abstract Aortic stenosis (AS) is a cause of significant morbidity and mortality worldwide. AS develops due to a combination of valve calcification and fibrosis. There are clear sex differences in the proportion of calcification and fibrosis that occurs, with females generally developing more significant AV fibrosis, and males developing more AV calcification. While the role of cardiovascular magnetic resonance (CMR) imaging is well established for the assessment of myocardial response to AS, there has been little work exploring the role of CMR for evaluation of the aortic valve (AV) tissue characteristics. While transthoracic echocardiography is the mainstay imaging modality for assessing AS, cardiac computed tomography (CCT) and CMR can be useful adjuncts. This exploratory work investigates the role of CMR in AS assessment, using T1 mapping to assess the degree of AV fibrosis in patients with severe AS and healthy controls, and compares CMR assessment to CCT. The CMR protocol included cardiac cine imaging. T1 mapping of the AV was then performed using the same slice used for AVA planimetry. CMR analysis was performed using cvi42 (Circle Cardiovascular Imaging, Canada). Some patients with severe AS underwent CCT imaging: CCT analysis of AV calcium scores were performed using 3mensio software©1. 202 patients with severe AS (34% females, 76[68,80] years) and 21 healthy controls (43% female, 68[61,71] years) were recruited to the study. Women and men with severe AS were age- and co-morbidity matched, and healthy controls were age-matched. There was no significant difference in AV peak velocity (Vmax) in the two AS cohorts on echocardiographic assessment (4.6 [4,5,4.7] m/sec). CCT was performed in 133 (66%) of the patients with severe AS, and women with severe AS had significantly lower CCT AV calcium scores compared to men with severe AS (512[340,750] vs 1351[799,1770];p<0.0001) (figure 1). Forty-seven of the patients with severe AS (23%) had native T1 mapping of the AV, and showed that women with severe AS had significantly higher mean native T1 values compared to men with severe AS (1910[1827,1993] vs 1815[1768,1860];p=0.042). Assessing all patients with severe AS, there was a moderate inverse correlation between CCT calcium score and native T1 mapping of the AV (r=-0.50, p=0.0014) (figure 2). There was no significant difference in AV native T1 values comparing both sexes with severe AS to their sex-matched controls (women with AS 1910[1827,1993] vs 1947[1926,2001];p=0.47. Men with AS 1815[1768,1860] vs 1840[1779,1901];p=0.60). Patients who have fibrosis-predominant AS cannot easily be detected with CCT. This work has indicated the potential role of native T1 mapping of the AV, to identify patients with severe AS without a high calcium burden. T1 mapping may prove useful in the detection of fibrosis-predominant AS.Figure1:CMR, TTE, and CCT data Figure2:Representative images

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.382
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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