The knowledge of vascular structures of port wine stains (PWSs) may be useful to select treatment doses and improve therapeutic efficacy. Biopsies are impractical to implement, therefore, it is necessary to develop non-invasive techniques for morphological evaluation. This study aimed to evaluate the application of a novel optical coherence tomography (OCT) system to characterize the vascular structures of PWSs. First, OCT images were obtained from the skin of healthy rabbit ears and compared with the histopathological images. Second, OCT was used to document the differences between PWS lesions and contralateral normal skin; the size and depth of the vascular structures of two clinical types of PWSs were measured and statistically analyzed. The dermal blood vessels of healthy rabbit ears were clearly distinguished from other tissue. There was no statistical difference between the vascular diameter or depth measured by OCT images and histopathological sections (P>0.05). The OCT images of the PWSs could be distinguished from normal skin. There was no statistical difference in the depth of vessels between the purple-type and the proliferative-type PWSs (P>0.05), while there was statistical difference in the diameter of vessels between them (P<0.01). Therefore, OCT is a promising, real-time, in vivo and non-invasive tool with which to characterize the vascular structures of PWSs.

A number of despeckling methods for optical coherence tomography (OCT) have been proposed. In these digital filtering techniques, speckle noise is often simplified as additive white Gaussian noise due to the logarithmic compression for the signal. The approximation is not completely consistent with the characteristic of OCT speckle noise, and cannot be reasonably extended to deconvolution algorithms. This paper presents a deconvolution model that combines the variational regularization term with the statistical characteristic constraints of data corrupted by OCT speckle noise. In the data fidelity term, speckle noise is modeled as signal dependent, and the point spread function of OCT systems is included. The regularization functional introduces a priori information on the original images, and a regularization term based on block matching 3D modeling is used to construct the variational model in the paper. Finally, the method is applied to the restoration of actual OCT raw data of human skin. The numerical results demonstrate that the proposed deconvolution algorithm can simultaneously enhance regions of images containing detail and remove OCT speckle noise.

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