Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model
Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model
复制标题
DOI：
10.1109/tci.2022.3183396
复制
发表时间：
2022-01-01
影响因子：
5.4
通讯作者：
Muramatsu，Shogo
中科院分区：
文献类型：
--
作者：
Kobayashi，Ruiki;Fujii，Genki;Muramatsu，Shogo
在这项研究中，一种新的恢复模型的光学相干层析成像（OCT）的数据提出。OCT装置使用近红外激光器以几微米的尺度获取样本的断层图像，并且已经被频繁地采用来测量生物组织的结构。在某些应用中，OCT设备面临反射光极其微弱的问题，需要借助图像处理来估计隐藏在各种噪声中的反射光的分布。OCT通过搜索峰值干涉位置及其强度来识别断层结构。因此，OCT数据恢复的挑战涉及识别干扰函数及其反卷积的问题。在这项研究中，恢复方法是通过减少问题的正则化最小二乘问题的潜折射率分布的硬约束，并使用原始-对偶分裂（PDS）框架的算法推导。PDS具有不需要逆矩阵运算的优点，并且能够处理高维数据。所提出的方法的意义进行了验证，通过模拟使用人工数据，然后进行实验，使用实际观察大小的体素。
In this study, a novel restoration model for the data of optical coherence tomography (OCT) is proposed. An OCT device acquires a tomographic image of a specimen at the scale of a few micrometers using a near-infrared laser and has been frequently adopted to measure the structures of bio-tissues. In certain applications, OCT devices face the problem of extremely weak reflected light and require the help of image processing to estimate the distribution of reflected light hidden in various noises. OCT identifies tomographic structures by searching for peak interference locations and their intensities. Therefore, the challenge of OCT data restoration involves the problem of identifying the interference function and its deconvolution. In this study, a restoration method is given by reducing the problem to a regularized least-squares problem with a hard constraint for the latent refractive index distributions, and an algorithm is derived using a primal-dual splitting (PDS) framework. The PDS has the advantage of requiring no inverse matrix operation and is able to handle high-dimensional data. The significance of the proposed method is verified through simulations using artificial data, followed by an experiment conducted using actual observation ofsized voxels.