ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2020
Automated quantification of myocardial tissue characteristics from native T
Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Society for Cardiovascular Magnetic Resonance reference values ("normal values") in cardiovascular magnetic resonance: 2025 update.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Guideline
- Towards fully automated synthetic ECV quantification: an open-access machine learning-based approach for fast blood draw-free CMR.Scientific reports · 2026Article
- Myocardial radiomics of non-ischemic cardiomyopathy using cardiovascular magnetic resonance: current perspectives and future directions.Frontiers in cardiovascular medicine · 2026Review
- Sex-specific body fat distribution predicts cardiovascular ageing.European heart journal · 2025Article
- Leveraging artificial intelligence for risk stratification of inherited cardiomyopathies in under-resourced settings.Heart rhythm O2 · 2025Article
- The Evolving Role of Cardiac Imaging in Hypertrophic Cardiomyopathy: Diagnosis, Prognosis, and Clinical Practice.Biomedicines · 2025Review
- Automated quantification of T1 and T2 relaxation times in liver mpMRI using deep learning: a sequence-adaptive approach.European radiology experimental · 2025Article
- Advanced Myocardial MRI Tissue Characterization Combining Contrast Agent-Free T1-Rho Mapping With Fully Automated Analysis.Journal of magnetic resonance imaging : JMRI · 2025Article
- Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance.Reviews in cardiovascular medicine · 2024Review
- Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data.Scientific reports · 2024Article
- Article
- Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty-guided space-time analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Article
- An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases.European heart journal. Digital health · 2023Article
- Temporal Uncertainty Localization to Enable Human-in-the-Loop Analysis of Dynamic Contrast-Enhanced Cardiac MRI Datasets.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Environmental and genetic predictors of human cardiovascular ageing.Nature communications · 2023Article
- Genetics of myocardial interstitial fibrosis in the human heart and association with disease.Nature genetics · 2023Article
- Incident Clinical and Mortality Associations of Myocardial Native T1 in the UK Biobank.JACC. Cardiovascular imaging · 2023Article
- Cardiovascular magnetic resonance for evaluation of cardiac involvement in COVID-19: recommendations by the Society for Cardiovascular Magnetic Resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Review
- Evaluating semi-supervision methods for medical image segmentation: applications in cardiac magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Introduction of a cascaded segmentation pipeline for parametric T1 mapping in cardiovascular magnetic resonance to improve segmentation performance.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundTissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Native T
methodsConvolutional neural networks (CNNs) with Bayesian inference are a category of artificial neural networks which model the uncertainty of the network output. This study presents an automated framework for tissue characterisation from native shortened modified Look-Locker inversion recovery ShMOLLI T
resultsT
conclusionsThe proposed pipeline allows for automatic analysis of myocardial native T
Indexed as
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Registered trials
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.