The presence and thickness of cardiac adipose tissue (CAT) has been extensively correlated with the progression of coronary artery disease. Although CAT is difficult to identify visually with ultrasound relative to other imaging modalities, it is desirable to bolster the efficacy of this modality due to its inexpensiveness and quick acquisition times. This study explores the viability of leveraging the increased visibility of CAT in MR images of the heart to label regions of CAT in ultrasound. This process entails using B-spline interpolation to address the anisotropy of MR volume segmentations, creating a continuous three-dimensional (3D) representation of the patient’s heart muscle and the surrounding CAT. The left myocardium, which is visible in both MRI and ultrasound, is then used to register an ultrasound scan from the same patient with the MRI-derived cardiac model. The intersection of the two-dimensional (2D) ultrasound plane with the 3D MRI-derived model allows regions in the ultrasound to be labelled according to tissue type. Spectral parameters are extracted from ultrasound data from these labelled regions and used to train a tissue type classifier. Using a random forest classifier, this process achieved a tissue classification accuracy of 76.67% for CAT, 80% for myocardium, and 63.33% for blood. The classifier achieved a sensitivity of 60.87% for CAT, 53.85% for myocardium, and 62.5% for blood. The specificity was 86.49% for CAT, 87.23% for myocardium, and 63.88% for blood.
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