Identification of vessel wall anomalies in thoracic aortic aneurysms through optical coherence tomography and gradient-based strategies

Abstract

Optical Coherence Tomography is a natural candidate for imaging biological structures just under tissue surface. Human thoracic aorta from aneurysms reveal elastin disorders and smooth muscle cell alterations when visualizing the media layer of the aortic wall, which is only some tens of microns in depth from surface. The resulting images require a suitable processing to enhance interesting disorder features and to use them as indicators for wall degradation, converting OCT into a hallmark for diagnosis of risk of aneurysm under intraoperative conditions. This work proposes gradient-based digital image processing approaches to conclude this risk. These techniques are believed to be useful in these applications as aortic wall disorders directly affect the refractive index of the tissue, having an effect on the gradient of the tissue reflectivity that conform the OCT image. Preliminary results show that the direction of the gradient contains information to estimate the tissue abnormality score. The detection of the edges of the OCT image is performed using the Canny algorithm. The edges delineate tissue disorders in the region of interest and isolate the abnormalities. These edges can be quantified to estimate a degradation score. Furthermore, the direction of the gradient seems to be a promising enhancement technique, as it detects areas of homogeneity in the region of interest. Automatic results from gradient-based strategies are finally compared to the histopathological global aortic score, which accounts for each risk factor presence and seriousness.