Effect of T1‐mapping technique and diminished image resolution on quantification of infarct mass and its ability in predicting appropriate <scp>ICD</scp> therapy
Notice bibliographique
Résumé
PURPOSE: Myocardial infarct (MI) may consist of an infarct core (IC) and a heterogeneous, semi-viable border zone (BZ). Patients with chronic MI in the left ventricular (LV) myocardium are at increased risk of developing ventricular arrhythmias, and may therefore qualify for implantable cardioverter defibrillator (ICD) therapy. Indices based on MI mass, as determined by cardiac magnetic resonance (CMR) imaging, are shown to be sensitive in predicting adverse ventricular arrhythmic events. However, several factors, such as imaging technique and spatial resolution affect the accuracy of MI mass quantification. The aim of this study was to compare the MI masses determined by T1-mapping CMR techniques to those of conventional late Gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) using inversion recovery fast gradient echo (IR-FGRE). We additionally aimed to investigate the effect of diminishing image resolution on quantification of the MI mass and its ability to predict appropriate ICD therapy. METHODS: Thirty-eight patients with known MI underwent acquisitions of three CMR imaging techniques: the multicontrast late enhancement (MCLE) and modified look-locker inversion recovery (MOLLI) T1-mapping techniques, and conventional inversion recovery fast gradient echo (IR-FGRE) about 20 min after double-dose injection of Gadolinium. We postprocessed images to quantify IC and BZ masses determined by each CMR technique using a full-width half-maximum (FWHM) approach in IR-FGRE images and a fuzzy c-means clustering algorithm for T1-mapping images. To determine the impact of spatial resolution in sensitivity of predicting ICD events, we artificially diminished resolution of MCLE images acquired from a separate group of 27 patients who had been followed up for ICD therapy and compared the MI masses estimated from the original and downsampled MCLE images. RESULTS: Twelve patients out of 27 (44%) received ICD therapy (i.e., one or more delivered shock) during the follow-up stage. Between each of the three imaging methods, IC masses were not significantly different. Conversely, BZ masses determined by MOLLI were larger compared to those determined by MCLE and IR-FGRE (P value = 0.0022 and 0.0003, respectively). The BZ masses determined by MCLE were not significantly different from those determined by IR-FGRE; however, BZ masses determined by the downsampled MCLE were significantly larger than those determined by IR-FGRE and original MCLE (P value = 0.0033 and 0.0003, respectively). The BZ mass estimated by original MCLE was larger in patients who had received ICD therapy compared to those who did not (P value = 0.044). However, when the spatial resolution of the MCLE images was diminished to that of MOLLI, BZ masses were not significantly different between patients with and without ICD therapy. CONCLUSIONS: While estimated IC masses were consistent among all three techniques, the estimated BZ masses were not consistent, especially when spatial resolution of images differed between the techniques. In particular, our study showed that diminished image resolution caused an increase in estimation of the BZ mass, likely due to partial volume effects, which led to a reduced sensitivity in the prediction of appropriate ICD therapy.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 ».