University of Houston

Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that is capable of producing high resolution cross-sectional images of interior of blood vessels and it is currently the gold standard technique for the assessment of the characteristics of the atherosclerotic plaques. Segmentation of IVUS images refers to the delineation of the lumen/intima and media/adventitia interfaces of the vessel. This process is necessary for assessing morphological characteristics of the vessel such as lumen diameter, minimum lumen cross-section area, and total atheroma volume. This information is crucial for making decisions such as whether a stent is needed to restore blood flow in an artery and to determine the characteristics of the stent. Other applications of IVUS include the study of mechanical properties of the vessel wall, characteristics of the plaque, and 3D reconstruction of the vessel. Segmentation of IVUS images may be performed manually by an expert observer. However, depending on the type of analysis, the number of frames to be segmented can range from a few frames to hundreds of frames.

In this dissertation, we present a unified computational method for the semi-automatic segmentation of the luminal/wall interface. The method can be used with either B-mode or RF-data and it is based on the deformation of a curve by optimizing a probabilistic cost function. The main contribution is the development of a physics-based inverse method for segmenting the RF data. Experimental results demonstrate the robustness and accuracy of the method. These results pave the way for the automation of the analysis of contrast-enhanced IVUS images to assess extra-luminal perfusion.