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Proceedings Paper

Computer-based assessment of right ventricular regional ejection fraction in patients with repaired Tetralogy of Fallot
Author(s): S.-K. Teo; S. T. Wong; M. L. Tan; Y. Su; L. Zhong; Ru-San Tan
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Paper Abstract

After surgical repair for Tetralogy of Fallot (TOF), most patients experience long-term complications as the right ventricle (RV) undergoes progressive remodeling that eventually affect heart functions. Thus, post-repair surgery is required to prevent further deterioration of RV functions that may result in malignant ventricular arrhythmias and mortality. The timing of such post-repair surgery therefore depends crucially on the quantitative assessment of the RV functions. Current clinical indices for such functional assessment measure global properties such as RV volumes and ejection fraction. However, these indices are less than ideal as regional variations and anomalies are obscured. Therefore, we sought to (i) develop a quantitative method to assess RV regional function using regional ejection fraction (REF) based on a 13-segment model, and (ii) evaluate the effectiveness of REF in discriminating 6 repaired TOF patients and 6 normal control based on cardiac magnetic resonance (CMR) imaging. We observed that the REF for the individual segments in the patient group is significantly lower compared to the control group (P < 0.05 using a 2-tail student t-test). In addition, we also observed that the aggregated REF at the basal, mid-cavity and apical regions for the patient group is significantly lower compared to the control group (P < 0.001 using a 2-tail student t-test). The results suggest that REF could potentially be used as a quantitative index for assessing RV regional functions. The computational time per data set is approximately 60 seconds, which demonstrates our method’s clinical potential as a real-time cardiac assessment tool.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941431 (20 March 2015); doi: 10.1117/12.2081839
Show Author Affiliations
S.-K. Teo, A*STAR Institute of High Performance Computing (Singapore)
S. T. Wong, A*STAR Institute of High Performance Computing (Singapore)
M. L. Tan, A*STAR Institute of High Performance Computing (Singapore)
Y. Su, A*STAR Institute of High Performance Computing (Singapore)
L. Zhong, National Heart Ctr. Singapore (Singapore)
Duke-NUS Graduate Medical School (Singapore)
Ru-San Tan, National Heart Ctr. Singapore (Singapore)
Duke-NUS Graduate Medical School (Singapore)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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