P2E-3 Blind Source Separation and K-Means Clustering for Vascular ARFI Image Segmentation, In Vivo and Ex Vivo

Abstract

Blind source separation (BSS) is a time domain method for signal decomposition previously demonstrated in medical ultrasound for adaptive regression filtering. Just as BSS is relevant to clutter rejection by differentiating RF signals from moving blood versus arterial wall tissue, BSS is useful for distinguishing displacement profiles measured in tissue exhibiting different mechanical responses to radiation force. In concert with K-means clustering, an algorithm for partitioning N data points into K subsets, BSS can be employed for automated image segmentation via mechanical property in acoustic radiation force impulse (ARFI) ultrasound. We present BSS-based ARFI image segmentation in application to the peripheral vasculature. First, our segmentation method is validated using a synthetic data set with additive noise. Second, our method is demonstrated in an excised atherosclerotic familial hypercholesterolemic (FH) pig iliac artery with confirmation by matched immunohistochemistry. Finally, the method is applied to segmenting in vivo ARFI images in an atherosclerotic FH pig iliac artery as well as a human popliteal vein with no known disease. This work substantiates additional applications of BSS-based ARFI image segmentation