Computer-aided infarction identification from cardiac CT images: A biomechanical approach with SVM

Abstract

Compared with global measurements such as ejection fraction, regional myocardial deformation can better aid detection of cardiac dysfunction. Although tagged and strain-encoded MR images can provide such regional information, they are uncommon in clinical routine. In contrast, cardiac CT images are more common with lower cost, but only provide motion of cardiac boundaries and additional constraints are required to obtain the myocardial strains. To verify the potential of contrast-enhanced CT images on computer-aided infarction identification, we propose a biomechanical approach combined with the support vector machine (SVM). A biomechanical model is used with deformable image registration to estimate 3D myocardial strains from CT images, and the regional strains and CT image intensities are input to the SVM classifier for regional infarction identification. Cross-validations on ten canine image sequences with artificially induced infarctions showed that the normalized radial and first principal strains were the most discriminative features, with respective classification accuracies of 87±13% and 84±10% when used with the normalized CT image intensity.