Cargando…

Automated quantification of myocardial tissue characteristics from native T(1) mapping using neural networks with uncertainty-based quality-control

BACKGROUND: Tissue 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(1) mapping in particular has shown promise a...

Descripción completa

Detalles Bibliográficos
Autores principales:	Puyol-Antón, Esther, Ruijsink, Bram, Baumgartner, Christian F., Masci, Pier-Giorgio, Sinclair, Matthew, Konukoglu, Ender, Razavi, Reza, King, Andrew P.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2020
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439533/ https://www.ncbi.nlm.nih.gov/pubmed/32814579 http://dx.doi.org/10.1186/s12968-020-00650-y

Ejemplares similares

Deep Learning for Classification and Selection of Cine CMR Images to Achieve Fully Automated Quality-Controlled CMR Analysis From Scanner to Report
por: Vergani, Vittoria, et al.
Publicado: (2021)

Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function
por: Ruijsink, Bram, et al.
Publicado: (2019)

Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank
por: Cecelja, Marina, et al.
Publicado: (2022)

The Role of AI in Characterizing the DCM Phenotype
por: Asher, Clint, et al.
Publicado: (2021)

An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases
por: Mariscal-Harana, Jorge, et al.
Publicado: (2023)

Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation
por: Puyol-Antón, Esther, et al.
Publicado: (2022)

Right ventricular function and adaption after hemi-Fontan completion in children with hypoplastic left heart syndrome
por: Ruijsink, Bram, et al.
Publicado: (2016)

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort
por: Wirth, Wolfgang, et al.
Publicado: (2020)

Uncertainty quantification in classical molecular dynamics
por: Wan, Shunzhou, et al.
Publicado: (2021)

Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain
por: Kemnitz, Jana, et al.
Publicado: (2019)

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning
por: Oksuz, Ilkay, et al.
Publicado: (2019)

Cardiac MRI to guide heart failure and atrial fibrillation drug discovery: a Mendelian randomization analysis
por: Schmidt, Amand, et al.
Publicado: (2023)

Detection of Differences in Longitudinal Cartilage Thickness Loss Using a Deep‐Learning Automated Segmentation Algorithm: Data From the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative
por: Eckstein, Felix, et al.
Publicado: (2022)

Uncertainty quantification
por: Chase, Luis
Publicado: (2019)

Automated Localization of Focal Ventricular Tachycardia From Simulated Implanted Device Electrograms: A Combined Physics–AI Approach
por: Monaci, Sofia, et al.
Publicado: (2021)

Pressure–volume loop-derived cardiac indices during dobutamine stress: a step towards understanding limitations in cardiac output in children with hypoplastic left heart syndrome()
por: Wong, James, et al.
Publicado: (2017)

Handbook of uncertainty quantification
por: Ghanem, Roger, et al.
Publicado: (2019)

Handbook of uncertainty quantification
por: Ghanem, Roger, et al.
Publicado: (2017)

Introduction to uncertainty quantification
por: Sullivan, T J
Publicado: (2015)

Head-to-head comparison of multiple cardiovascular magnetic resonance techniques for the detection and quantification of intramyocardial haemorrhage in patients with ST-elevation myocardial infarction
por: Pavon, Anna Giulia, et al.
Publicado: (2020)

Rapid quantification of myocardial fibrosis: a new macro-based automated analysis
por: Hadi, Awal M., et al.
Publicado: (2011)

Rapid Quantification of Myocardial Fibrosis: A New Macro-Based Automated Analysis
por: Hadi, Awal M., et al.
Publicado: (2010)

Impact of the Choice of Native T(1) in Pixelwise Myocardial Blood Flow Quantification
por: Kräuter, Corina, et al.
Publicado: (2020)

Routine 3D SSFP cine imaging for improved analysis of myocardial volumetry and deformation
por: Ruijsink, Bram, et al.
Publicado: (2016)

Quantification of coronary vessel wall thickness using a flexible time-resolved golden angle dual-inversion recovery acquisition for facilitated sequence timing at 3T
por: Ginami, Giulia, et al.
Publicado: (2016)

Dobutamine stress testing in patients with Fontan circulation augmented by biomechanical modeling
por: Ruijsink, Bram, et al.
Publicado: (2020)

Uncertainty Quantification of Reactivity Scales
por: Proppe, Jonny, et al.
Publicado: (2022)

Strain Tracking with Uncertainty Quantification
por: Kim, Younhun, et al.
Publicado: (2023)

Automated advanced imaging in acute ischemic stroke. Certainties and uncertainties
por: Fainardi, Enrico, et al.
Publicado: (2023)

Peri-infarct edema leads to overestimation of myocardial salvage in late reperfused myocardial infarction
por: Carbone, Iacopo, et al.
Publicado: (2013)

Uncertainty quantification for hyperbolic and kinetic equations
por: Jin, Shi, et al.
Publicado: (2017)

Uncertainty quantification and stochastic modeling with Matlab
por: Souza de Cursi, Eduardo, et al.
Publicado: (2015)

Uncertainty quantification: theory, implementation, and applications
por: Smith, Ralph C
Publicado: (2014)

Uncertainty quantification in computational fluid dynamics
por: Bijl, Hester, et al.
Publicado: (2013)

Stochastic Systems: Uncertainty Quantification and Propagation
por: Grigoriu, Mircea
Publicado: (2012)

Information Theoretic Quantification of Diagnostic Uncertainty
por: Westover, M Brandon, et al.
Publicado: (2012)

Uncertainty Quantification in Irreversible Electroporation Simulations
por: Labarbera, Nicholas
Publicado: (2017)

Evaluation of Uncertainty Quantification in Deep Learning
por: Ståhl, Niclas, et al.
Publicado: (2020)

The importance of uncertainty quantification in model reproducibility
por: Volodina, Victoria, et al.
Publicado: (2021)

Uncertainty quantification patterns for multiscale models
por: Ye, D., et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_97tqju1q0d7chnsu56bh0a07ve