Abstract
This paper presents an automated method for regional wall motion abnormality detection (RWMA) from rest and stress cardiac MRI. The automated RWMA detection is based on a statistical shape model of myocardial contraction trained on slice-based myocardial contours from in ED and ES. A combination of rigid and non-rigid registrations is introduced to align a patient shape to the normokinetic myocardium model, where pure contractility information is kept. The automated RWMA method is applied to identify potentially infarcted myocardial segments from rest–stress MRI alone.
In this study, 41 cardiac MRI studies of healthy subjects were used to build the statistical normokinetic model, while 12 myocardial infarct patients were included for validation. The rest–stress data produced a better separation between scar and normal segments compared to the rest–only data. The sensitivity, specificity and accuracy were increased by 34%, 30%, and 32%, respectively. The area under the ROC curve for the rest–stress data was improved to 0.87 compared to 0.63 for the rest–only data.
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Suinesiaputra, A. et al. (2011). Slice-Based Combination of Rest and Dobutamine–Stress Cardiac MRI Using a Statistical Motion Model to Identify Myocardial Infarction: Validation against Contrast-Enhanced MRI. In: Metaxas, D.N., Axel, L. (eds) Functional Imaging and Modeling of the Heart. FIMH 2011. Lecture Notes in Computer Science, vol 6666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21028-0_33
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DOI: https://doi.org/10.1007/978-3-642-21028-0_33
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