Multidimensional OCT Imaging Enabled by Compressed Sensing

总结 光学相干层析成像系统时间速度和分辨率的最新进展 为动态生物过程和成像的延时成像开辟了新的可能性 具有微米级分辨率的大表面积样品。谱域OCT（SD-OCT）和扫频OCT 源OCT（SS-OCT）系统经常以远高于100 kHz和高达400 kHz的a线速率而自豪。的 更高的扫描速率的结果是减少了运动伪影并且还有更高的数据吞吐量。现代 内窥镜OCT系统提出了导致1GB/s以上数据速率的扫描速率。这种方法 现代PC的数据传输速率和写入速度。在采集时压缩此数据将具有 对这些具体挑战的巨大影响。OCT成像中处理的任务种类越来越多， 涉及使用可能是多维的高分辨率数据集，其中维度包括空间 （x，y，z）、时间和对比度（偏振、弹性成像、相敏、多普勒）。压缩感知（CS） 提供了处理这些海量数据集的工具，方法是对远低于奈奎斯特极限的数据进行采样， 数据吞吐量由于压缩感知可以减少所需的数据量， 压缩传感从图像中提取必要的信息，彻底改变了磁共振 成像（MRI），加速数据采集，从而缩短扫描时间，从而节省成本。那里 已经初步演示了在OCT中使用压缩感知。我们的目标是开发一个 这是CS-OCT的一种综合方法，允许对具有各种结构的样本进行成像。最近 OCT硬件的进步导致了更好的图像质量。然而，结合压缩感知， 我们相信，OCT的全部功能可以用于需要大视场成像的应用中， 长期成像以评估治疗效果、动力学和对器官系统的扰动。 我们在这个项目中的目标是利用预测编码来展示压缩感知可以被利用 在具有组织类型多样性的心脏样品中，同时产生>75%的压缩率。在这 小赠款R 03提案，我们的目标是开发，测试和演示压缩传感的新方法 可在具有心脏成像数据集的图像中使用的OCT。我们的两个目标包括：1）压缩 感测以实现大视场体积3D OCT成像，以及2）压缩感测对 提取组织特征和结构。

Summary Recent advances in the temporal speed and resolution of optical coherence tomography (OCT) imaging systems have opened the door for new possibilities in time-lapse imaging of dynamic biological processes and imaging samples with large surface area with micron scale resolution. Both spectral domain OCT (SD-OCT) and swept source OCT (SS-OCT) systems frequently boast a-line rates well above 100 kHz and as high as 400 kHz. The consequence of higher scan-rates is reduced motion artifacts and also higher data through-put. Modern endoscopic OCT system are proposing scan-rates that result in data rates upwards of 1GB/s. This approaches the data transfer rate and write speeds of modern PCs. Compressing this data at acquisition would have a massive impact on these specific challenges. The kinds of tasks being tackled in OCT imaging increasingly involve working with high-resolution datasets which could be multi-dimensional, where dimensions include space (x,y,z), time, and contrast (polarization, elastography, phase sensitive, doppler). Compressed Sensing (CS) provides tools for dealing with these massive datasets by sampling data far below the Nyquist limit to reduce data through-put. Because of the potential of compressed sensing to reduce the required amount of data to extract the necessary information within an image, compressed sensing has revolutionized magnetic resonance imaging (MRI), accelerating data acquisition which results in shorted scan times and thus cost savings. There have been initial demonstrations on the use of compressed sensing in OCT. It is our goal to develop a comprehensive approach for CS-OCT that will allow for imaging samples with a variety of architecture. Recent advances in OCT hardware have resulted in better image quality. However, combined with compressed sensing, we believe the full power of OCT can be utilized within applications that require large field of view imaging or imaging over long periods of time to assess therapeutic effects, dynamics, and perturbations to organ systems. Our goal within this project is to utilize predictive coding to showcase that compressed sensing can be utilized within cardiac samples that have diversity of tissue types, while yielding >75% compression rate. Within this small grant R03 proposal, we aim to develop, test, and demonstrate a new approach for compressed sensing OCT that can be utilized within images with cardiac imaging datasets. Our two aims include: 1) Compressed sensing to enable large field of view volumetric 3D OCT imaging and 2) Impact of compressed sensing on extracting tissue features and architecture.