Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；School of Information Science and Engineering, Shandong Normal University（山东师范大学信息科学与工程学院）；Department of Biomedical Engineering, Oregon Health & Science University（俄勒冈健康与科学大学生物医学工程系）
Min Gao, Yukun Guo, Tristan T. Hormel, Jiande Sun, Thomas Hwang, Yali Jia
Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3x3- or 6x6-mm. Compared to 3x3-mm angiograms with proper sampling density, 6x6-mm angiograms have significantly lower scan quality, with reduced signal-to-noise ratio and worse shadow artifacts due to undersampling. Here, we propose a deep-learning-based high-resolution angiogram reconstruction network (HARNet) to generate enhanced 6x6-mm superficial vascular complex (SVC) angiograms. The network was trained on data from 3x3-mm and 6x6-mm angiograms from the same eyes. The reconstructed 6x6-mm angiograms have significantly lower noise intensity and better vascular connectivity than the original images. The algorithm did not generate false flow signal at the noise level presented by the original angiograms. The image enhancement produced by our algorithm may improve biomarker measurements and qualitative clinical assessment of 6x6-mm OCTA.
Optical coherence tomography angiography (OCTA) has drawn numerous attentions in ophthalmology. However, its data acquisition is time-consuming, because it is based on temporal-decorrelation principle thus requires multiple repeated volumetric OCT scans. In this paper, we developed a deep learning algorithm by combining a fovea attention mechanism with a residual neural network, which is able to extract capillary-level angiograms directly from a single OCT scan. The segmentation results of the inner limiting membrane and outer plexiform layers and the central $1\times1$ mm$^2$ field of view of…
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；University of Liverpool（利物浦大学）；University of Southern California（南加州大学）；Southern University of Science and Technology（南方科技大学）；University of Chinese Academy of Sciences（中国科学院大学）
Optical Coherence Tomography Angiography (OCTA) has been increasingly used in the management of eye and systemic diseases in recent years. Manual or automatic analysis of blood vessel in 2D OCTA images (en face angiograms) is commonly used in clinical practice, however it may lose rich 3D spatial distribution information of blood vessels or capillaries that are useful for clinical decision-making. In this paper, we introduce a novel 3D vessel reconstruction framework based on the estimation of vessel depth maps from OCTA images. First, we design a network with structural constraints to predict…
Optical coherence tomography angiography (OCTA) requires high transverse sampling density for visualizing retinal and choroidal capillaries. Low transverse sampling causes resolution degradation, such as the angiograms in wide-field OCTA. In this paper, we propose to address this problem using deep learning. We conducted extensive experiments on converting the centrally cropped 3 x 3 mm2 field of view (FOV) of the 8 x 8 mm2 foveal OCTA images (a sampling density of 22.9 $μ$m) to the native 3 x 3 mm2 en face OCTA images (a sampling density of 12.2 $μ$m). We employed a cycle-consistent adversari…
Existing methods to reconstruct vascular structures from a computed tomography (CT) angiogram rely on injection of intravenous contrast to enhance the radio-density within the vessel lumen. However, pathological changes can be present in the blood lumen, vessel wall or a combination of both that prevent accurate reconstruction. In the example of aortic aneurysmal disease, a blood clot or thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in 70-80% of cases. These deformations prevent the automatic extraction of vital clinically relevant information by current m…
University of Alabama（阿拉巴马大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Nanyang Technological University（南洋理工大学）；The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Shanghai Jiao Tong University（上海交通大学）
Coronary artery disease (CAD) is a cardiovascular condition with high morbidity and mortality. Intravascular optical coherence tomography (IVOCT) has been considered as an optimal imagining system for the diagnosis and treatment of CAD. Constrained by Nyquist theorem, dense sampling in IVOCT attains high resolving power to delineate cellular structures/ features. There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging. In this paper, we propose a viable spectral-spatial acquisition method that down-scales the sampling process in both spectral and spatia…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute（加州纳米系统研究所）；University of Houston（休斯顿大学）；Baylor College of Medicine（贝勒医学院）
Optical Coherence Tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical set-up and can be easily integrated with existing swept-source or spectral domain OCT systems to reduce the amount of raw spectral data to be acquired. To sh…
Beijing Institute of Technology（北京理工大学）；Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所）；Cixi Institute of Biomedical Engineering（慈溪生物医学工程研究所）；Ningbo University of Technology（宁波工程学院）
Retinal Optical Coherence Tomography Angiography (OCTA) with high-resolution is important for the quantification and analysis of retinal vasculature. However, the resolution of OCTA images is inversely proportional to the field of view at the same sampling frequency, which is not conducive to clinicians for analyzing larger vascular areas. In this paper, we propose a novel Sparse-based domain Adaptation Super-Resolution network (SASR) for the reconstruction of realistic 6x6 mm2/low-resolution (LR) OCTA images to high-resolution (HR) representations. To be more specific, we first perform a simp…
Reducing the bit-depth is an effective approach to lower the cost of optical coherence tomography (OCT) systems and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit-depth will lead to the degeneration of the detection sensitivity thus reduce the signal-to-noise ratio (SNR) of OCT images. In this paper, we propose to use deep learning for the reconstruction of the high SNR OCT images from the low bit-depth acquisition. Its feasibility was preliminarily evaluated by applying the proposed method to the quantized $3\sim8$-bit data from native 12-bit int…
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Baidu Inc（百度公司）；Southern University of Science and Technology（南方科技大学）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Precise characterization and analysis of iris shape from Anterior Segment OCT (AS-OCT) are of great importance in facilitating diagnosis of angle-closure-related diseases. Existing methods focus solely on analyzing structural properties identified from the 2D slice, while accurate characterization of morphological changes of iris shape in 3D AS-OCT may be able to reveal in addition the risk of disease progression. In this paper, we propose a novel framework for reconstruction and quantification of 3D iris surface from AS-OCT imagery. We consider it to be the first work to detect angle-closure …
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；University of Electronic Science and Technology of China（电子科技大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Nanjing University of Science and Technology（南京理工大学）
Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration has long been the subject of research. This is commonly achieved by obtaining multiple undersampled images, simultaneously, through parallel imaging. In this paper, we propose the Dual-Octave Network (DONet), which is capable of learning multi-scale spatial-frequency features from both the real and imaginary components of MR data, for fast parallel MR image reconstruction. More specifically, our DONet consists of a series of Dual-Octave convolutions (Dual-OctConv), which are connected in a dense ma…
WAVE-UNET: Wavelength based Image Reconstruction method using attention UNET for OCT images
Institute of Optical Materials and Technologies, Bulgarian Academy of Sciences（保加利亚科学院光学材料与技术研究所）；Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息技术与通信科学学院）；Electronics and Telecommunications Research Institute（电子与电信研究所）
Usher Institute, University of Edinburgh（爱丁堡大学厄舍研究所）；Princess Alexandra Eye Pavilion, NHS Lothian（洛锡安国民保健制度亚历山德拉公主眼科馆）；Edinburgh Imaging, University of Edinburgh（爱丁堡大学爱丁堡影像中心）；Health Data Research UK（英国健康数据研究所）；School of Clinical Sciences, University of Edinburgh（爱丁堡大学临床科学学院）；Centre for Clinical Brain Sciences, University of Edinburgh（爱丁堡大学临床脑科学中心）
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality for the visualisation of microvasculature in vivo that has encountered broad adoption in retinal research. OCTA potential in the assessment of pathological conditions and the reproducibility of studies relies on the quality of the image analysis. However, automated segmentation of parafoveal OCTA images is still an open problem. In this study, we generate the first open dataset of retinal parafoveal OCTA images with associated ground truth manual segmentations. Furthermore, we establish a standard for OCTA…
Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing modified state-of-the-art octave convolutional layers into the U-net backbone. The xQSM method was compared with recentlyproposed U-net-based and conventional regularizationbased methods, using peak signal to noise ratio (PSNR), st…
Optical coherence tomography (OCT) is a volumetric imaging modality that empowers clinicians and scientists to noninvasively visualize the cross-sections of biological samples. As the latest generation of its kind, Fourier-domain OCT (FD-OCT) offers a micrometer-scale axial resolution by taking advantage of coherence gating. Based on the current theory, it is believed the only way to obtain a higher-axial-resolution OCT image is to physically extend the system's spectral bandwidth given a certain central wavelength. Here, we showed the belief is wrong. We proposed a novel reconstruction framew…
Spectral domain optical coherence tomography (OCT) offers high resolution multidimensional imaging, but generally suffers from defocussing, intensity falloff and shot noise, causing artifacts and image degradation along the imaging depth. In this work, we develop an iterative statistical reconstruction technique, based upon the interferometric synthetic aperture microscopy (ISAM) model with additive noise, to actively compensate for these effects. For the ISAM re-sampling, we use a non uniform FFT with Kaiser-Bessel interpolation, offering efficiency and high accuracy. We then employ an accele…
We customize an end-to-end image compression framework for retina OCT images based on deep convolutional neural networks (CNNs). The customized compression scheme consists of three parts: data Preprocessing, compression CNNs, and reconstruction CNNs. Data preprocessing module reduces the speckle noise of the OCT images and the segments out the region of interest. We added customized skip connections between the compression CNNs and the reconstruction CNNs to reserve the detail information and trained the two nets together with the semantic segmented image patches from data preprocessing module…
We propose a non-convex variational model for the super-resolution of Optical Coherence Tomography (OCT) images of the murine eye, by enforcing sparsity with respect to suitable dictionaries learnt from high-resolution OCT data. The statistical characteristics of OCT images motivate the use of α-stable distributions for learning dictionaries, by considering the non-Gaussian case, α=1. The sparsity-promoting cost function relies on a non-convex penalty - Cauchy-based or Minimax Concave Penalty (MCP) - which makes the problem particularly challenging. We propose an efficient algorithm for minimi…
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Massachusetts Institute of Technology（麻省理工学院）；Tufts School of Medicine（塔夫茨医学院）
Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature. Based on repeated optical coherence tomography (OCT) scans, intensity changes are observed over time and used to compute OCTA image data. OCTA data are prone to noise and artifacts caused by variations in flow speed and patient movement. We propose a novel iterative maximum a posteriori signal recovery algorithm in order to generate OCTA volumes with reduced noise and increased image quality. This algorithm is based on previous work on probabilistic…
Institute of Medical Technology, Hamburg University of Technology（汉堡工业大学医学技术研究所）
Purpose. Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside the tissue. For this purpose, fiber-optical sensors can be directly integrated into the needle tip. Optical coherence tomography (OCT) can be used to image tissue. Here, we study how to calibrate OCT to sense forces, e.g. during robotic needle placement. Methods. We investigate whether using raw spectral OCT data without a typical image reconstruction can improve a deep learning-based calibration between optical signal…
Optical coherence tomography (OCT) has seen widespread success as an in vivo clinical diagnostic 3D imaging modality, impacting areas including ophthalmology, cardiology, and gastroenterology. Despite its many advantages, such as high sensitivity, speed, and depth penetration, OCT suffers from several shortcomings that ultimately limit its utility as a 3D microscopy tool, such as its pervasive coherent speckle noise and poor lateral resolution required to maintain millimeter-scale imaging depths. Here, we present 3D optical coherence refraction tomography (OCRT), a computational extension of O…
Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review
Department of Mechanical and Electronic Engineering, Kunming University of Science and Technology（昆明理工大学机械电子工程系）；Eye Clinics, Yunnan First People's Hospital（云南第一人民医院眼科）
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）
Various deep learning models have been developed to segment anatomical structures from medical images, but they typically have poor performance when tested on another target domain with different data distribution. Recently, unsupervised domain adaptation methods have been proposed to alleviate this so-called domain shift issue, but most of them are designed for scenarios with relatively small domain shifts and are likely to fail when encountering a large domain gap. In this paper, we propose DCDA, a novel cross-modality unsupervised domain adaptation framework for tasks with large domain shif…
West Virginia University（西弗吉尼亚大学）；Rockefeller Neuroscience Institute（洛克菲勒神经科学研究所）；Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）
In this paper, we design a Generative Adversarial Network (GAN)-based solution for super-resolution and segmentation of optical coherence tomography (OCT) scans of the retinal layers. OCT has been identified as a non-invasive and inexpensive modality of imaging to discover potential biomarkers for the diagnosis and progress determination of neurodegenerative diseases, such as Alzheimer's Disease (AD). Current hypotheses presume the thickness of the retinal layers, which are analyzable within OCT scans, can be effective biomarkers. As a logical first step, this work concentrates on the challeng…
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局资讯通信研究院）；Southern University of Science and Technology（南方科技大学）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）
Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a self-reconstruction framework, which tends to learn an identity mapping and reduces the sensitivity to anomalies. To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images. Specifically, we use an intermediate proxy to bridge the input image and the reconstructed image. We study different proxy types, and we find that the sup…
Simon Fraser University（西蒙弗雷泽大学）；Wake Forest University School of Medicine（维克森林大学医学院）；University of British Columbia（不列颠哥伦比亚大学）；University College London（伦敦大学学院）
Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and in…
Point cloud is a crucial representation of 3D contents, which has been widely used in many areas such as virtual reality, mixed reality, autonomous driving, etc. With the boost of the number of points in the data, how to efficiently compress point cloud becomes a challenging problem. In this paper, we propose a set of significant improvements to patch-based point cloud compression, i.e., a learnable context model for entropy coding, octree coding for sampling centroid points, and an integrated compression and training process. In addition, we propose an adversarial network to improve the unifo…
