Atlas-Based Quantification of Cardiac Remodeling Due to Myocardial Infarction
By Xingyu Zhang, B. Cowan, David A. Bluemke, J. Finn, Carissa G. Fonseca, A. Kadish, Daniel C. Lee, J. Lima, Avan Suinesiaputra, A. Young and 1 more. 2014
82 citations recorded by the source corpus.
Myocardial infarction leads to changes in the geometry (remodeling) of the left ventricle (LV) of the heart. The degree and type of remodeling provides important diagnostic information for the therapeutic management of ischemic heart disease. In this paper, we present a novel analysis framework for characterizing remodeling after myocardial infarction, using LV shape descriptors derived from atlas-based shape models. Cardiac magnetic resonance images from 300 patients with myocardial infarction and 1991 asymptomatic volunteers were obtained from the Cardiac Atlas Project. Finite element models were customized to the spatio-temporal shape and function of each case using guide-point modeling. Principal component analysis was applied to the shape models to derive modes of shape variation across all cases. A logistic regression analysis was performed to determine the modes of shape variation most associated with myocardial infarction. Goodness of fit results obtained from end-diastolic and end-systolic shapes were compared against the traditional clinical indices of remodeling: end-diastolic volume, end-systolic volume and LV mass. The combination of end-diastolic and end-systolic shape parameter analysis achieved the lowest deviance, Akaike information criterion and Bayesian information criterion, and the highest area under the receiver operating characteristic curve. Therefore, our framework quantitatively characterized remodeling features associated with myocardial infarction, better than current measures. These features enable quantification of the amount of remodeling, the progression of disease over time, and the effect of treatments designed to reverse remodeling effects.
Subfields: Cardiology and Cardiovascular Medicine, Surgery
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In Cardiology and Cardiovascular Medicine
Citations in the corpus
- Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling (2018, background): “These modes are then employed in the discrimination of distinct groups of subjects by their shape differences or to identify the ones mostly associated with diseases [10,11,12].”
- Statistical shape modeling of the left ventricle: myocardial infarct classification challenge (2017, background): “While these factors are known to have effects on LV mass and volume, these are expected to be outweighed by disease processes [20], [28].”
- Orthogonal decomposition of left ventricular remodeling in myocardial infarction (2017, unknown): “Smaller deviance, AIC, and BIC, and larger AUC are indicative of better goodness-of-fit.”