Aortic valve imaging using cardiac magnetic resonance including T1 mapping in the assessment of severe aortic stenosis
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».