A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data
Arrhythmogenic left ventricular cardiomyopathy is a genetic myocardial disease difficult to diagnose due to the lack of gold standard criteria. Simultaneous PET/MR imaging, combined with multiparametric quantitative analysis, could facilitate the identification of different profiles related to the phenotype and progression of cardiomyopathy. This preliminary study focuses on a methodological strategy for dealing with PET/MRI data, including inter-patient data linkage and regional analysis. Two-step clustering was applied to T1 and T2 maps, LGE, and 18F-FDG-PET images of 99 patients genetically
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- LinkedLinked via arxiv author · 85%Brunnhilde Ponsi →
“A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data”
- LinkedLinked via arxiv author · 85%Thomas Carlier →
“A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data”
- LinkedLinked via arxiv author · 85%Lara Marteau →
“A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data”
- LinkedLinked via arxiv author · 85%Aurélien Monnet →
“A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data”
- LinkedLinked via arxiv author · 85%Thomas Eugène →
“A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data”
- LinkedLinked via arxiv author · 85%Jean-Michel Serfaty →
“A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data”
- LinkedLinked via arxiv author · 85%Nicolas Piriou →
“A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data”
- LinkedLinked via arxiv author · 85%Hatem Necib →
“A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data”