Deep OCT Angiography Image Generation for Motion Artifact Suppression

Eye movements, blinking and other motion during the acquisition of optical coherence tomography (OCT) can lead to artifacts, when processed to OCT angiography (OCTA) images. Affected scans emerge as high intensity (white) or missing (black) regions, resulting in lost information. The aim of this research is to fill these gaps using a deep generative model for OCT to OCTA image translation relying on a single intact OCT scan. Therefore, a U-Net is trained to extract the angiographic information from OCT patches. At inference, a detection algorithm finds outlier OCTA scans based on their surroundings, which are then replaced by the trained network. We show that generative models can augment the missing scans. The augmented volumes could then be used for 3-D segmentation or increase the diagnostic value.Comment: Accepted at BVM 202

Design of a portable wide field of view GPU-accelerated multiphoton imaging system for real-time imaging of breast surgical specimens

We present a portable multiphoton system designed for evaluating centimeter-scale surgical margins on surgical breast specimens in a clinical setting. The system is designed to produce large field of view images at a high frame rate, while using GPU processing to render low latency, video-rate virtual H&E images for real-time assessment. The imaging system and virtual H&E rendering algorithm are demonstrated by imaging unfixed human breast tissue in a clinical setting

Modularization of deep networks allows cross-modality reuse: lesson learnt

Fundus photography and Optical Coherence Tomography Angiography (OCT-A) are two commonly used modalities in ophthalmic imaging. With the development of deep learning algorithms, fundus image processing, especially retinal vessel segmentation, has been extensively studied. Built upon the known operator theory, interpretable deep network pipelines with well-defined modules have been constructed on fundus images. In this work, we firstly train a modularized network pipeline for the task of retinal vessel segmentation on the fundus database DRIVE. The pretrained preprocessing module from the pipeline is then directly transferred onto OCT-A data for image quality enhancement without further fine-tuning. Output images show that the preprocessing net can balance the contrast, suppress noise and thereby produce vessel trees with improved connectivity in both image modalities. The visual impression is confirmed by an observer study with five OCT-A experts. Statistics of the grades by the experts indicate that the transferred module improves both the image quality and the diagnostic quality. Our work provides an example that modules within network pipelines that are built upon the known operator theory facilitate cross-modality reuse without additional training or transfer learning.European Union. Horizon 2020 Research and Innovation Programme (Grant 810316

Optical Coherence Tomography Angiography Characteristics of Iris Melanocytic Tumors

Purpose To evaluate tumor vasculature with optical coherence tomography angiography (OCTA) in malignant iris melanomas and benign iris lesions. Design Cross-sectional observational clinical study. Participants Patients with iris lesions and healthy volunteers. Methods Eyes were imaged using OCTA systems operating at 1050- and 840-nm wavelengths. Three-dimensional OCTA scans were acquired. Iris melanoma patients treated with radiation therapy were imaged again after I-125 plaque brachytherapy at 6 and 18 months. Main Outcome Measures OCT and OCTA images, qualitative evaluation of iris and tumor vasculature, and quantitative vessel density. Results One eye each of 8 normal volunteers and 9 patients with iris melanomas or benign iris lesions, including freckles, nevi, and an iris pigment epithelial (IPE) cyst, were imaged. The normal iris has radially oriented vessels within the stroma on OCTA. Penetration of flow signal in normal iris depended on iris color, with best penetration seen in light to moderately pigmented irides. Iris melanomas demonstrated tortuous and disorganized intratumoral vasculature. In 2 eyes with nevi there was no increased vascularity; in another, fine vascular loops were noted near an area of ectropion uveae. Iris freckles and the IPE cyst did not have intrinsic vascularity. The vessel density was significantly higher within iris melanomas (34.5%±9.8%, P < 0.05) than in benign iris nevi (8.0%±1.4%) or normal irides (8.0%±1.2%). Tumor regression after radiation therapy for melanomas was associated with decreased vessel density. OCTA at 1050 nm provided better visualization of tumor vasculature and penetration through thicker tumors than at 840 nm. But in very thick tumors and highly pigmented lesions even 1050-nm OCTA could not visualize their full thickness. Interpretable OCTA images were obtained in 82% of participants in whom imaging was attempted. Conclusions This is the first demonstration of OCTA in iris tumors. OCTA may provide a dye-free, no-injection, cost-effective method for monitoring a variety of tumors, including iris melanocytic lesions, for growth and vascularity. This could be helpful in evaluating tumors for malignant transformation and response to treatment. Penetration of the OCT beam remains a limitation for highly pigmented tumors, as does the inability to image the entire iris in a single field.National Institutes of Health (U.S.) (Grants R01 EY023285, R01 EY024544, R01 EY018184, DP3 DK104397, UL1TR000128, and P30 EY01057

Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising

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 OCTA signal models and maximum likelihood estimates. Reconstruction results using total variation minimization and wavelet shrinkage for regularization were compared against an OCTA ground truth volume, merged from six co-registered single OCTA volumes. The results show a significant improvement in peak signal-to-noise ratio and structural similarity. The presented algorithm brings together OCTA image generation and Bayesian statistics and can be developed into new OCTA image generation and denoising algorithms.German Research Foundation (Grant (MA 4898/12-1)National Institutes of Health (U.S.) (Grant 5-R01- EY011289-31

QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data

© Springer International Publishing AG 2017. Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise. This paper introduces a spatio-temporal denoising algorithm for OCT data on a B-scan level using a novel quantile sparse image (QuaSI) prior. To remove speckle noise while preserving image structures of diagnostic relevance, we implement our QuaSI prior via median filter regularization coupled with a Huber data fidelity model in a variational approach. For efficient energy minimization, we develop an alternating direction method of multipliers (ADMM) scheme using a linearization of median filtering. Our spatio-temporal method can handle both, denoising of single B-scans and temporally consecutive B-scans, to gain volumetric OCT data with enhanced signal-to-noise ratio. Our algorithm based on 4 B-scans only achieved comparable performance to averaging 13 B-scans and outperformed other current denoising methods

Deep OCT Angiography Image Generation for Motion Artifact Suppression

Part of the Informatik aktuell book series (INFORMAT)Eye movements, blinking and other motion during the acquisition of optical coherence tomography (OCT) can lead to artifacts, when processed to OCT angiography (OCTA) images. Affected scans emerge as high intensity (white) or missing (black) regions, resulting in lost information. The aim of this research is to fill these gaps using a deep generative model for OCT to OCTA image translation relying on a single intact OCT scan. Therefore, a U-Net is trained to extract the angiographic information from OCT patches. At inference, a detection algorithm finds outlier OCTA scans based on their surroundings, which are then replaced by the trained network. We show that generative models can augment the missing scans. The augmented volumes could then be used for 3-D segmentation or increase the diagnostic value

Virtual Hematoxylin and Eosin Transillumination Microscopy Using Epi-Fluorescence Imaging

We derive a physically realistic model for the generation of virtual transillumination, white light microscopy images using epi-fluorescence measurements from thick, unsectioned tissue. We demonstrate this technique by generating virtual transillumination H&E images of unsectioned human breast tissue from epi-fluorescence multiphoton microscopy data. The virtual transillumination algorithm is shown to enable improved contrast and color accuracy compared with previous color mapping methods. Finally, we present an open source implementation of the algorithm in OpenGL, enabling real-time GPU-based generation of virtual transillumination microscopy images using conventional fluorescence microscopy systems

OCT-OCTA segmentation: combining structural and blood flow information to segment Bruch’s membrane

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement In this paper we present a fully automated graph-based segmentation algorithm that jointly uses optical coherence tomography (OCT) and OCT angiography (OCTA) data to segment Bruch's membrane (BM). This is especially valuable in cases where the spatial correlation between BM, which is usually not visible on OCT scans, and the retinal pigment epithelium (RPE), which is often used as a surrogate for segmenting BM, is distorted by pathology. We validated the performance of our proposed algorithm against manual segmentation in a total of 18 eyes from healthy controls and patients with diabetic retinopathy (DR), non-exudative age-related macular degeneration (AMD) (early/intermediate AMD, nascent geographic atrophy (nGA) and drusen-associated geographic atrophy (DAGA) and geographic atrophy (GA)), and choroidal neovascularization (CNV) with a mean absolute error of ∼0.91 pixel (∼4.1 µm). This paper suggests that OCT-OCTA segmentation may be a useful framework to complement the growing usage of OCTA in ophthalmic research and clinical communities