Processing, segmentation, and visualization of pre- and post-stent coronary arteries using intra-vascular ultrasound

Intra-vascular ultrasound (IVUS) has significant potential for providing new information about the structure and condition of the coronary arteries. However, these image datasets are inherently noisy and characterized by frequent drop-outs and shadowing, considerably constraining their use for quantitative evaluation of coronary artery integrity and stent augmentation. A feasibility study was conducted to test the effectiveness of a software toolkit for processing, segmenting, and visualizing pre-and post-stent IVUS datasets, as a precursor to detailed quantitative evaluation of IVUS with regard to characterization of coronary luminal wall properties. Frame averaging, histogram processing, and anisotropic diffusion were used to reduce speckle noise and compensate for image dropout and shadowing. A region of interest (ROI) analysis was conducted to determine the effectiveness of the image processing. Preliminary results suggest that the image processing steps are effective at increasing the contrast to speckle ratio. In addition, contrast between the arterial wall and lumen was improved, producing an edge enhancement effect. Image registration was used to align images within a volume (i.e. 2-D registration) and between volumes (i.e. 3-D registration). A voxel matching method was used for the 2-D registration and a surface matching method was used for 3-D registration. A morphological connect method for segmentation was used to extract large structures in the data. A new edge-based algorithm was developed to segment the data in regions where the other method failed. Volume rendering methods were used to visualize the data. These preliminary visualizations of processed, segmented and registered IVUS datasets illustrated encouraging potential for further quantitative analysis of coronary artery morphology and pathology.