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首页> 外文会议>Optical Coherence tomography and coherence domain optical methods in biomedicine XIX >Axial resolution improvement in Spectral Domain Optical Coherence Tomography using a depth-adaptive maximum-a-posterior framework
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Axial resolution improvement in Spectral Domain Optical Coherence Tomography using a depth-adaptive maximum-a-posterior framework

机译：使用深度自适应最大值后验框架提高光谱域光学相干断层扫描的轴向分辨率

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The axial resolution of Spectral Domain Optical Coherence Tomography (SD-OCT) images degrades with scanning depth due to the limited number of pixels and the pixel size of the camera, any aberrations in the spectrometer optics and wavelength dependent scattering and absorption in the imaged object. Here we propose a novel algorithm which compensates for the blurring effect of these factors of the depth-dependent axial Point Spread Function (PSF) in SD-OCT images. The proposed method is based on a Maximum A Posteriori (MAP) reconstruction framework which takes advantage of a Stochastic Fully Connected Conditional Random Field (SFCRF) model. The aim is to compensate for the depth-dependent axial blur in SD-OCT images and simultaneously suppress the speckle noise which is inherent to all OCT images. Applying the proposed depth-dependent axial resolution enhancement technique to an OCT image of cucumber considerably improved the axial resolution of the image especially at higher imaging depths and allowed for better visualization of cellular membrane and nuclei. Comparing the result of our proposed method with the conventional Lucy-Richardson deconvolution algorithm clearly demonstrates the efficiency of our proposed technique in better visualization and preservation of fine details and structures in the imaged sample, as well as better speckle noise suppression. This illustrates the potential usefulness of our proposed technique as a suitable replacement for the hardware approaches which are often very costly and complicated.
机译：光谱域光学相干断层扫描（SD-OCT）图像的轴向分辨率会随着扫描深度的下降而降低，这是由于像素数量有限和相机的像素大小，光谱仪光学系统中的任何像差以及与成像对象有关的波长相关的散射和吸收。在这里，我们提出了一种新颖的算法，该算法可以补偿SD-OCT图像中与深度相关的轴向点扩展函数（PSF）的这些因素的模糊效果。所提出的方法基于最大后验（MAP）重建框架，该框架利用了随机完全连接条件随机场（SFCRF）模型。目的是补偿SD-OCT图像中与深度有关的轴向模糊，并同时抑制所有OCT图像固有的斑点噪声。将提出的深度相关的轴向分辨率增强技术应用于黄瓜的OCT图像，可以显着提高图像的轴向分辨率，尤其是在较高的成像深度处，并且可以更好地显示细胞膜和细胞核。将我们提出的方法的结果与传统的Lucy-Richardson反卷积算法进行比较，清楚地证明了我们提出的技术在更好地可视化和保留成像样本中的细部和结构以及更好地抑制斑点噪声方面的效率。这说明了我们提出的技术作为通常非常昂贵和复杂的硬件方法的合适替代品的潜在用途。

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