Doppler optical coherence tomography (DOCT) is a promising functional imaging modality for quantitative blood flow measurement. A major limitation of DOCT is that only the axial component of flow can be measured, which is strongly influenced by the geometry of the vasculature. To overcome this challenge, we proposed a new method to retrieve absolute blood flow velocity of the vascular networks with fully restored topology. The application of open active snake can detect the skeleton of vasculature network without the need of vasculature segmentation, allowing Doppler angle to be calculated. The discontinuity of vasculature induced by Doppler angle and the limited dynamic range is corrected by a tensor voting based method, enabling the measurement of absolute blood flow velocity along each fully connected vessel branch. We present the results of in vivo cerebral blood flow (CBF) networks to demonstrate the efficacy of the proposed method.
Optical coherence tomography(OCT) imaging of bladder is gaining recognization due to the capability of noninvasive cross-sectional imaging of the bladder at the micron-level resolution and a relatively large field of view. Previous studies have shown the potential of OCT image to enhance detection of bladder transitional cell carcinoma(TCC). However, quantitative OCT image analysis for affirmative identification of bladder tumor remains a challenge. Here, we report a novel method to enhance detection of TCC based on OCT images by analyzing anatomical and textural alteration of bladder. Specifically, OCT images are first processed with Dual Tree Complex Wavelet Transform denoising algorithms to reduce image speckle noise. Then, the layer segmentation method that mainly based on a dual path graph searching algorithm is performed on the denoised images to delineate three layers of bladder. The segmentation results show improved effectiveness and robustness in comparison to conventional graph theory based method. With layer segmentation, multiple measurements including layer thickness and texture can be quantified. The significant difference in quantified metrics between TCC and normal bladder indicate the potential use of those metrics for TCC identification. The proposed method provides valuable insights into TCC and has the potential to enhance the detection of tumor in the clinic.
Optical coherence tomography angiography (OCTA) is a promising tool for imaging subsurface microvascular networks owing to its micron-level resolution and high sensitivity. However, it is not uncommon that OCTA imaging tend to suffer from strip artifacts induced by tissue motion. Although various algorithms for motion correction have been reported, a method that enables motion correction on a single en face OCTA image remains a challenge. In this study, we proposed a novel motion correction approach based on microvasculature detection and broken gap filling. Unlike previous methods using registration to restore disturbed vasculature during motion artifact removal, tensor voting is performed in individual projected image to connect the broken vasculature. Both simulation and in vivo 3D OCTA imaging of mouse bladder are performed to validate the effectiveness of this method. A comparison of in vivo images before and after motion correction shows that our method effectively corrects tissue motion artifacts while preserving continuity of vasculature network. Furthermore, in vivo results of this technique are presented to demonstrate the utility for imaging tumor angiogenesis in the mouse bladder.