Medical Device Development Center, Osong Medical Innovation Foundation, Cheongju, Chungbuk, Republic of Korea.
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Lasers Surg Med. 2022 Aug;54(6):895-906. doi: 10.1002/lsm.23543. Epub 2022 Apr 2.
Optical coherence tomography (OCT) is a cross-sectional imaging method utilizing a low coherence interferometry. The lateral resolution of the OCT is limited by the numerical aperture (NA) of the imaging lens. Using a high NA lens improves the lateral resolution but reduces the depth of focus (DOF). In this study, we propose a method to improve the lateral resolution of OCT images by end-to-end training of a deep 1-D deconvolution network without use of high-resolution images.
To improve the lateral resolution of the OCT, we trained the 1-D deconvolution network using lateral profiles of OCT images and the beam spot size. We used our image-guided laparoscopic surgical tool (IGLaST) to acquire OCT images of nonbiological and biological samples ex vivo. The OCT images were then blurred by applying Gaussian functions with various full width half maximums ranging from 40 to 160 µm. The network was trained using the blurred OCT images as input and the non-blurred original OCT images as output. We quantitatively evaluated the developed network in terms of similarity and signal-to-ratio (SNR), using in-vivo images of mesenteric tissue from a porcine model that was not used for training. In addition, we performed knife-edge tests and qualitative evaluation of the network to show the lateral resolution improvement of ex-vivo and in-vivo OCT images.
The proposed method showed an improvement of image quality on both blurred images and non-blurred images. When the proposed deconvolution network was applied, the similarity to the non-blurred image was improved by 1.29 times, and the SNR was improved by 1.76 dB compared to the artificially blurred images, which was superior to the conventional deconvolution method. The knife-edge tests at distances at 200 to 1000 µm from the imaging probe showed an approximately 1.2 times improvement in lateral resolution. In addition, through qualitative evaluation, it was found that the image quality of both ex-vivo and in-vivo tissue images was improved with clear structure and less noise.
This study showed the ability of the 1-D deconvolution network to improve the image quality of OCT images with variable lateral resolution. We were able to train the network with a small amount of data by constraining the network in 1-D. The quantitative evaluation showed better results than conventional deconvolution methods for various amount of blurring. Qualitative evaluation showed analogous results with quantitative results. This simple yet powerful image restoration method provides improved lateral resolution and suppresses background noise, making it applicable to a variety of OCT imaging applications.
光学相干断层扫描（OCT）是一种利用低相干干涉测量法的横截面成像方法。OCT 的横向分辨率受成像透镜的数值孔径（NA）限制。使用高 NA 透镜可提高横向分辨率，但会降低景深（DOF）。本研究提出了一种通过端到端训练深度一维反卷积网络来提高 OCT 图像横向分辨率的方法，而无需使用高分辨率图像。
为了提高 OCT 的横向分辨率，我们使用 OCT 图像的横向轮廓和光斑大小来训练一维反卷积网络。我们使用图像引导腹腔镜手术工具（IGLaST）获取非生物和生物样本的 OCT 图像。然后，通过应用具有 40 至 160 μm 全宽半高值的高斯函数对 OCT 图像进行模糊处理。该网络使用模糊的 OCT 图像作为输入，原始未模糊的 OCT 图像作为输出进行训练。我们使用来自猪模型的非训练用活体肠组织图像，从相似性和信噪比（SNR）的角度定量评估所开发的网络。此外，我们还进行了刀口测试和网络的定性评估，以显示离体和活体 OCT 图像的横向分辨率提高。
该方法在模糊图像和非模糊图像上均提高了图像质量。与人工模糊图像相比，应用所提出的反卷积网络后，与非模糊图像的相似性提高了 1.29 倍，SNR 提高了 1.76 dB，优于传统的反卷积方法。在距成像探头 200 至 1000 μm 的距离处进行的刀口测试显示，横向分辨率提高了约 1.2 倍。此外，通过定性评估发现，离体和活体组织图像的质量均得到改善，结构清晰，噪声减少。
本研究表明，一维反卷积网络能够提高具有可变横向分辨率的 OCT 图像的图像质量。通过在一维方向上约束网络，我们可以用少量数据训练网络。与各种程度模糊的传统反卷积方法相比，定量评估结果更好。定性评估结果与定量结果类似。这种简单而强大的图像恢复方法提供了更高的横向分辨率和抑制背景噪声，使其适用于各种 OCT 成像应用。
