Quantitative prediction of improvement in cardiac function after revascularization with MR imaging and modeling: initial results.

PURPOSE: To evaluate a model that can be used quantitatively to predict changes in postrevascularization left ventricular function based on classification of myocardial tissue as hibernating, scarred, or normal with cine magnetic resonance (MR) imaging. MATERIALS AND METHODS: Eleven patients with chronic left ventricular dysfunction were studied before and after revascularization with cine MR imaging. Regional myocardial contractility and wall thickness were used in the model to predict postrevascularization ejection fraction (EF). The actual EF from the postrevascularization MR images was compared with the EF from the prerevascularization images predicted with the model by using regression analysis and Bland-Altman analysis. RESULTS: Correlation between the actual EF after revascularization and the EF predicted by using the model yielded an R value of 0.98, with a standard error of 1.3 EF percentage points. Predicting changes in function in a myocardial segment was less successful because only 55% of segments classified as hibernating actually improved resting function after revascularization. In nonimproved segments, 78% were either adjacent to infarcted segments or had nontransmural wall thinning. CONCLUSION: A simple mathematical model combined with functional information provided by MR imaging was used to predict improvements in global EF resulting from revascularization.