Marie N Dusch1, Samir R Thadani1, Gupreet S Dhillon1, Michael D Hope2. 1. Department of Radiology, University of California, San Francisco, USA. 2. Department of Radiology, University of California, San Francisco, USA. Electronic address: michael.hope@ucsf.edu.
Abstract
INTRODUCTION: Diastolic dysfunction contributes significantly to diastolic heart failure. We examined the use of cardiac magnetic resonance imaging (CMR) using midwall longitudinal fractional shortening (MLFS) in the evaluation of transthoracic echocardiogram (TTE)-evidenced diastolic dysfunction. METHODS: A total of 80 patients with CMR within 6 months of TTE and normal ejection fraction were identified. MLFS was calculated as percentage change in distance from the anterior mitral leaflet base to the apical endocardium in systole and diastole. RESULTS: MLFS of grade II/III [0.14 (0.12-0.16)] was significantly lower than that of grade 0/I [0.20 (0.19-0.21)], P=.001. MLFS detected age-related changes with grade 0 [0.22 (0.21-0.23)] significantly lower than grade I [0.18 (0.16-0.20)], P=.001. CONCLUSION: TTE-evidenced diastolic dysfunction can be reliably identified by CMR using MLFS.
INTRODUCTION:Diastolic dysfunction contributes significantly to diastolic heart failure. We examined the use of cardiac magnetic resonance imaging (CMR) using midwall longitudinal fractional shortening (MLFS) in the evaluation of transthoracic echocardiogram (TTE)-evidenced diastolic dysfunction. METHODS: A total of 80 patients with CMR within 6 months of TTE and normal ejection fraction were identified. MLFS was calculated as percentage change in distance from the anterior mitral leaflet base to the apical endocardium in systole and diastole. RESULTS: MLFS of grade II/III [0.14 (0.12-0.16)] was significantly lower than that of grade 0/I [0.20 (0.19-0.21)], P=.001. MLFS detected age-related changes with grade 0 [0.22 (0.21-0.23)] significantly lower than grade I [0.18 (0.16-0.20)], P=.001. CONCLUSION: TTE-evidenced diastolic dysfunction can be reliably identified by CMR using MLFS.
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