Determination of regional ejection fraction in XXXX with @entity583 by using merged late @entity2287 enhancement and cine MR: feasibility study.

@entity1 @entity583 @entity2287 @entity1188
PURPOSE: To quantify regional ejection fraction (EF) in @entity1 with @entity583 ( @entity583 ) by using merged late @entity2287 enhancement (LGE) and cine magnetic resonance (MR) imaging, and compare this method with the standardized 17-segment American Heart Association approach. MATERIALS AND METHODS: After institutional review board approval and informed consent, 15 @entity583 @entity1 (14 @entity1 , one @entity1 ; mean age, 63 years +/- 10 [standard deviation]) were studied at 1 week and at 4 months after @entity583 . Short-axis LGE MR information was used to quantify @entity1188 size, and to divide the left ventricle (LV) on short-axis cine MR images in @entity1188 , periinfarct, and remote regions by using a fixed-center method, yielding information on regional volumes, regional EFs, systolic wall thickening (SWT), and systolic wall motion (SWM). This approach was compared with a floating-center approach and the 17-segment approach. RESULTS: Mean @entity1188 size (normalized to LV mass) was 25% +/- 14 at 1 week and 16% +/- 8 at 4 months (P < .001). At 4 months, LV EF significantly improved (mean, 47.9% +/- 5.9 vs 50.9% +/- 6.6, P = .031), matching an improvement of regional EF (mean, 17.1% +/- 11.5 vs 24.6% +/- 13.1, P = .005) and SWM (mean, 3.2 mm +/- 1.7 vs 3.9 mm +/- 2.1, P = .027) in the @entity1188 . No significant changes in regional EF, SWT, or SWM occurred in the remote myocardium. Regional EF estimates correlated well with SWT and SWM (both r = 0.92, P < .001). The floating-center method invariably underestimated regional EF (mean, -20.8% +/- 7.6; 95% confidence interval: -23.7%, -17.9%), especially with increasing @entity1188 size. By using the 17-segment approach, no functional improvement was shown in the @entity1188 . CONCLUSION: Assessment of regional ventricular performance (regional EF) in well-defined areas (eg, @entity1188 ) is feasible by using merged LGE and cine MR imaging.
