ArticleScientific reports2017
Morphologically normalized left ventricular motion indicators from MRI feature tracking characterize myocardial infarction.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.Nature machine intelligence · 2025Article
- Effects of Long-Term Endurance Exercise on Cardiac Morphology, Function, and Injury Indicators among Amateur Marathon Runners.International journal of environmental research and public health · 2023Article
- Geometry Does Impact on the Plane Strain Directions of the Human Left Ventricle, Irrespective of Disease.Journal of cardiovascular development and disease · 2022Article
- Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis.Frontiers in cardiovascular medicine · 2021Article
- Combining Statistical Shape Model and Principal Component Analysis to Estimate Left Ventricular Volume and Ejection Fraction.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- A Statistical Shape Model Approach for Computing Left Ventricle Volume and Ejection Fraction Using Multi-plane Ultrasound Images.VipIMAGE 2019 : proceedings of the VII ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, October 16-18, 2019, Porto, Portugal. VipIMAGE (Conference) (2019 : Porto, Portugal) · 2019Article
- Deep learning cardiac motion analysis for human survival prediction.Nature machine intelligence · 2019Article
- Machine Learning Approaches for Myocardial Motion and Deformation Analysis.Frontiers in cardiovascular medicine · 2019Review
- 3D myocardial deformation analysis from cine MRI as a marker of amyloid protein burden in cardiac amyloidosis: validation versus T1 mapping.The international journal of cardiovascular imaging · 2018Article
- Gaussian Process Regressions for Inverse Problems and Parameter Searches in Models of Ventricular Mechanics.Frontiers in physiology · 2018Article
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7 authors.
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Abstract
We characterized motion attributes arising from LV spatio-temporal analysis of motion distributions in myocardial infarction. Time-varying 3D finite element shape models were obtained in 300 Controls and 300 patients with myocardial infarction. Inter-individual left ventricular shape differences were eliminated using parallel transport to the grand mean of all cases. The first three principal component (PC) scores were used to characterize trajectory attributes. Scores were tested with ANOVA/MANOVA using patient disease status (Infarcts vs. Controls) as a factor. Infarcted patients had significantly different magnitude, orientation and shape of left ventricular trajectories in comparison to Controls. Significant differences were found for the angle between PC scores 1 and 2 in the endocardium, and PC scores 1 and 3 in the epicardium. The largest differences were found in the magnitude of endocardial motion. Endocardial PC scores in shape space showed the highest classification power using support vector machine, with higher total accuracy in comparison to previous methods. Shape space performed better than size-and-shape space for both epicardial and endocardial features. In conclusion, LV spatio-temporal motion attributes accurately characterize the presence of infarction. This approach is easily generalizable to different pathologies, enabling more precise study of the pathophysiological consequences of a wide spectrum of cardiac diseases.
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