Computer-based assessment of left ventricular regional ejection fraction in patients after myocardial infarction

S.-K. Teo; Y. Su; R.S. Tan; L. Zhong

doi:10.1117/12.2043241

Abstract

After myocardial infarction (MI), the left ventricle (LV) undergoes progressive remodeling which adversely affects heart function and may lead to development of heart failure. There is an escalating need to accurately depict the LV remodeling process for disease surveillance and monitoring of therapeutic efficacy. Current practice of using ejection fraction to quantitate LV function is less than ideal as it obscures regional variation and anomaly. Therefore, we sought to (i) develop a quantitative method to assess LV regional ejection fraction (REF) using a 16-segment method, and (ii) evaluate the effectiveness of REF in discriminating 10 patients 1-3 months after MI and 9 normal control (sex- and agematched) based on cardiac magnetic resonance (CMR) imaging. Late gadolinium enhancement (LGE) CMR scans were also acquired for the MI patients to assess scar extent. We observed that the REF at the basal, mid-cavity and apical regions for the patient group is significantly lower as compared to the control group (P < 0.001 using a 2-tail student t-test). In addition, we correlated the patient REF over these regions with their corresponding LGE score in terms of 4 categories – High LGE, Low LGE, Border and Remote. We observed that the median REF decreases with increasing severity of infarction. The results suggest that REF could potentially be used as a discriminator for MI and employed to measure myocardium homogeneity with respect to degree of infarction. The computational performance per data sample took approximately 25 sec, which demonstrates its clinical potential as a real-time cardiac assessment tool.

Citation Download Citation

S.-K. Teo, S.-K. Teo, Y. Su, Y. Su, R.S. Tan, R.S. Tan, L. Zhong, L. Zhong, "Computer-based assessment of left ventricular regional ejection fraction in patients after myocardial infarction", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903520 (18 March 2014); doi: 10.1117/12.2043241; https://doi.org/10.1117/12.2043241

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