Distributed scanning volumetric SDOCT for motion corrected corneal biometry

Algorithm and software for field distortion correction in a commercial SD-OCT for corneal curvature assessment

Accurate assessment of corneal curvatures using frequency domain optical coherence tomography (OCT) with galvanometer scanners remains challenging due to the well-known scan field distortion. This paper presents an algorithm and software for correcting the distortion using only two simple measurements in which a readily available standard sphere is positioned in different depths in front of the OCT scanner. This offers a highly accessible and easily reproducible method for the field distortion correction (FDC). The correction was validated by measuring different spherical phantoms and conducting corneal curvature measurements of ex vivo porcine corneas using a commercial spectral-domain OCT system and a clinically approved swept-source OCT as a reference instrument. Thus, the error in radius measurements of spherical phantoms was reduced by >90% and astigmatism by >80% using FDC. In explanted porcine eyes, the error in astigmatism measurements with the Telesto was reduced by 75% for power and 70% for angle. The best fitting sphere radius was determined up to a deviation of 0.4% from the Anterion. This paper describes a correction algorithm for OCT immanent distortion that is applicable to any scanning OCT setup and enables precise corneal curvature measurements. The MATLAB software for the FDC is publicly available on GitHub.

Simultaneous alignment and surface regression using hybrid 2D-3D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations

Layer segmentation is important to quantitative analysis of retinal optical coherence tomography (OCT). Recently, deep learning based methods have been developed to automate this task and yield remarkable performance. However, due to the large spatial gap and potential mismatch between the B-scans of an OCT volume, all of them were based on 2D segmentation of individual B-scans, which may lose the continuity and diagnostic information of the retinal layers in 3D space. Besides, most of these methods required dense annotation of the OCT volumes, which is labor-intensive and expertise-demanding. This work presents a novel framework based on hybrid 2D-3D convolutional neural networks (CNNs) to obtain continuous 3D retinal layer surfaces from OCT volumes, which works well with both full and sparse annotations. The 2D features of individual B-scans are extracted by an encoder consisting of 2D convolutions. These 2D features are then used to produce the alignment displacement vectors and layer segmentation by two 3D decoders coupled via a spatial transformer module. Two losses are proposed to utilize the retinal layers' natural property of being smooth for B-scan alignment and layer segmentation, respectively, and are the key to the semi-supervised learning with sparse annotation. The entire framework is trained end-to-end. To the best of our knowledge, this is the first work that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs. Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.

Effect of fixational eye movements in corneal topography measurements with optical coherence tomography

There is an increasing interest in applying optical coherence tomography (OCT) to quantify the topography of ocular structures. However, in its most usual configuration, OCT data is acquired sequentially while a beam is scanned through the region of interest, and the presence of fixational eye movements can affect the accuracy of the technique. Several scan patterns and motion correction algorithms have been proposed to minimize this effect, but there is no consensus on the ideal parameters to obtain a correct topography. We have acquired corneal OCT images with raster and radial patterns, and modeled the data acquisition in the presence of eye movements. The simulations replicate the experimental variability in shape (radius of curvature and Zernike polynomials), corneal power, astigmatism, and calculated wavefront aberrations. The variability of the Zernike modes is highly dependent on the scan pattern, with higher variability in the direction of the slow scan axis. The model can be a useful tool to design motion correction algorithms and to determine the variability with different scan patterns.

Higher-order regression three-dimensional motion-compensation method for real-time optical coherence tomography volumetric imaging of the cornea

SIGNIFICANCE

Optical coherence tomography (OCT) allows high-resolution volumetric three-dimensional (3D) imaging of biological tissues in vivo. However, 3D-image acquisition can be time-consuming and often suffers from motion artifacts due to involuntary and physiological movements of the tissue, limiting the reproducibility of quantitative measurements.

AIM

To achieve real-time 3D motion compensation for corneal tissue with high accuracy.

APPROACH

We propose an OCT system for volumetric imaging of the cornea, capable of compensating both axial and lateral motion with micron-scale accuracy and millisecond-scale time consumption based on higher-order regression. Specifically, the system first scans three reference B-mode images along the C-axis before acquiring a standard C-mode image. The difference between the reference and volumetric images is compared using a surface-detection algorithm and higher-order polynomials to deduce 3D motion and remove motion-related artifacts.

RESULTS

System parameters are optimized, and performance is evaluated using both phantom and corneal (ex vivo) samples. An overall motion-artifact error of <4.61 microns and processing time of about 3.40 ms for each B-scan was achieved.

CONCLUSIONS

Higher-order regression achieved effective and real-time compensation of 3D motion artifacts during corneal imaging. The approach can be expanded to 3D imaging of other ocular tissues. Implementing such motion-compensation strategies has the potential to improve the reliability of objective and quantitative information that can be extracted from volumetric OCT measurements.

Reconstruction of Optical Coherence Tomography Images Using Mixed Low Rank Approximation and Second Order Tensor Based Total Variation Method

This paper proposes a mixed low-rank approximation and second-order tensor-based total variation (LRSOTTV) approach for the super-resolution and denoising of retinal optical coherence tomography (OCT) images through effective utilization of nonlocal spatial correlations and local smoothness properties. OCT imaging relies on interferometry, which explains why OCT images suffer from a high level of noise. In addition, data subsampling is conducted during OCT A-scan and B-scan acquisition. Therefore, using effective super-resolution algorithms is necessary for reconstructing high-resolution clean OCT images. In this paper, a low-rank regularization approach is proposed for exploiting nonlocal self-similarity prior to OCT image reconstruction. To benefit from the advantages of the redundancy of multi-slice OCT data, we construct a third-order tensor by extracting the nonlocal similar three-dimensional blocks and grouping them by applying the k-nearest-neighbor method. Next, the nuclear norm is used as a regularization term to shrink the singular values of the constructed tensor in the non-local correlation direction. Further, the regularization approaches of the first-order tensor-based total variation (FOTTV) and SOTTV are proposed for better preservation of retinal layers and suppression of artifacts in OCT images. The alternative direction method of multipliers (ADMM) technique is then used to solve the resulting optimization problem. Our experiments show that integrating SOTTV instead of FOTTV into a low-rank approximation model can achieve noticeably improved results. Our experimental results on the denoising and super-resolution of OCT images demonstrate that the proposed model can provide images whose numerical and visual qualities are higher than those obtained by using state-of-the-art methods.

N2NSR-OCT: Simultaneous denoising and super-resolution in optical coherence tomography images using semisupervised deep learning

Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal-to-noise ratio (SNR) and high-resolution (HR) OCT images within a short scanning time, we presented a learning-based method to recover high-quality OCT images from noisy and low-resolution OCT images. We proposed a semisupervised learning approach named N2NSR-OCT, to generate denoised and super-resolved OCT images simultaneously using up- and down-sampling networks (U-Net (Semi) and DBPN (Semi)). Additionally, two different super-resolution and denoising models with different upscale factors (2× and 4×) were trained to recover the high-quality OCT image of the corresponding down-sampling rates. The new semisupervised learning approach is able to achieve results comparable with those of supervised learning using up- and down-sampling networks, and can produce better performance than other related state-of-the-art methods in the aspects of maintaining subtle fine retinal structures.

Eye motion correction algorithm for OCT-based corneal topography

With its sequential image acquisition, OCT-based corneal topography is often susceptible to measurement errors due to eye motion. We have developed a novel algorithm to detect eye motion and minimize its impact on OCT topography maps. We applied the eye motion correction algorithm to corneal topographic scans acquired using a 70 kHz spectral-domain OCT device. OCT corneal topographic measurements were compared to those from a rotating Scheimpflug camera topographer. The motion correction algorithm provided a 2-4 fold improvement in the repeatability of OCT topography and its agreement with the standard Scheimpflug topographer. The repeatability of OCT Zernike-based corneal mean power, cardinal astigmatism, and oblique astigmatism after motion detection was 0.14 D, 0.28 D, and 0.24 D, respectively. The average differences between the two devices were 0.19 D for simulated keratometry-based corneal mean power, 0.23 D for cardinal astigmatism, and 0.25 D for oblique astigmatism. Our eye motion detection method can be applied to any OCT device, and it therefore represents a powerful tool for improving OCT topography.

Biometric Measurement of Anterior Segment: A Review

Biometric measurement of the anterior segment is of great importance for the ophthalmology, human eye modeling, contact lens fitting, intraocular lens design, etc. This paper serves as a comprehensive review on the historical development and basic principles of the technologies for measuring the geometric profiles of the anterior segment. Both the advantages and drawbacks of the current technologies are illustrated. For in vivo measurement of the anterior segment, there are two main challenges that need to be addressed to achieve high speed, fine resolution, and large range imaging. One is the motion artefacts caused by the inevitable and random human eye movement. The other is the serious multiple scattering effects in intraocular turbid media. The future research perspectives are also outlined in this paper.

Super-Resolution of Optical Coherence Tomography Images by Scale Mixture Models

In this paper, a new statistical model is proposed for the single image super-resolution of retinal Optical Coherence Tomography (OCT) images. OCT imaging relies on interfero-metry, which explains why OCT images suffer from a high level of noise. Moreover, data subsampling is carried out during the acquisition of OCT A-scans and B-scans. So, it is necessary to utilize effective super-resolution algorithms to reconstruct high-resolution clean OCT images. In this paper, a nonlocal sparse model-based Bayesian framework is proposed for OCT restoration. For this reason, by characterizing nonlocal patches with similar structures, known as a group, the sparse coefficients of each group of OCT images are modeled by the scale mixture models. In this base, the coefficient vector is decomposed into the point-wise product of a random vector and a positive scaling variable. Estimation of the sparse coefficients depends on the proposed distribution for the random vector and scaling variable where the Laplacian random vector and Generalized Extreme-Value (GEV) scale parameter (Laplacian+GEV model) show the best goodness of fit for each group of OCT images. Finally, a new OCT super-resolution method based on this new scale mixture model is introduced, where the maximum a posterior estimation of both sparse coefficients and scaling variables are calculated efficiently by applying an alternating minimization method. Our experimental results prove that the proposed OCT super-resolution method based on the Laplacian+GEV model outperforms other competing methods in terms of both subjective and objective visual qualities.

Retinal imaging in human autopsy eyes using a custom optical coherence tomography periscope

Age-related macular degeneration (AMD) is a major cause of vision loss in the elderly. To better study the pathobiology of AMD, postmortem eyes offer an excellent opportunity to correlate optical coherence tomography (OCT) imaging characteristics with histopathology. However, postmortem eyes from autopsy present challenges to standard OCT imaging including opaque anterior segment structures and standard of care autopsy processing resulting in oblique views to the macula. To overcome these challenges, we report a custom periscope attached by a standard mount to an OCT sample arm and demonstrate high quality macular OCT acquisitions in autopsy-processed eyes.

Golden angle based scanning for robust corneal topography with OCT

Corneal topography allows the assessment of the cornea's refractive power which is crucial for diagnostics and surgical planning. The use of optical coherence tomography (OCT) for corneal topography is still limited. One limitation is the susceptibility to disturbances like blinking of the eye. This can result in partially corrupted scans that cannot be evaluated using common methods. We present a new scanning method for reliable corneal topography from partial scans. Based on the golden angle, the method features a balanced scan point distribution which refines over measurement time and remains balanced when part of the scan is removed. The performance of the method is assessed numerically and by measurements of test surfaces. The results confirm that the method enables numerically well-conditioned and reliable corneal topography from partially corrupted scans and reduces the need for repeated measurements in case of abrupt disturbances.

Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes

Patient motion artifacts are often visible in densely sampled or large wide field-of-view (FOV) retinal optical coherence tomography (OCT) volumes. A popular strategy for reducing motion artifacts is to capture two orthogonally oriented volumetric scans. However, due to larger volume sizes, longer acquisition times, and corresponding larger motion artifacts, the registration of wide FOV scans remains a challenging problem. In particular, gaps in data acquisition due to eye motion, such as saccades, can be significant and their modeling becomes critical for successful registration. In this article, we develop a complete computational pipeline for the automatic motion correction and accurate registration of wide FOV orthogonally scanned OCT images of the human retina. The proposed framework utilizes the retinal boundary segmentation as a guide for registration and requires only a minimal transformation of the acquired data to produce a successful registration. It includes saccade detection and correction, a custom version of the optical flow algorithm for dense lateral registration and a linear optimization approach for axial registration. Utilizing a wide FOV swept source OCT system, we acquired retinal volumes of 12 subjects and we provide qualitative and quantitative experimental results to validate the state-of-the-art effectiveness of the proposed technique. The source code corresponding to the proposed algorithm is available online.

Pupil Tracking for Real-Time Motion Corrected Anterior Segment Optical Coherence Tomography

Volumetric acquisition with anterior segment optical coherence tomography (ASOCT) is necessary to obtain accurate representations of the tissue structure and to account for asymmetries of the anterior eye anatomy. Additionally, recent interest in imaging of anterior segment vasculature and aqueous humor flow resulted in application of OCT angiography techniques to generate en face and 3D micro-vasculature maps of the anterior segment. Unfortunately, ASOCT structural and vasculature imaging systems do not capture volumes instantaneously and are subject to motion artifacts due to involuntary eye motion that may hinder their accuracy and repeatability. Several groups have demonstrated real-time tracking for motion-compensated in vivo OCT retinal imaging, but these techniques are not applicable in the anterior segment. In this work, we demonstrate a simple and low-cost pupil tracking system integrated into a custom swept-source OCT system for real-time motion-compensated anterior segment volumetric imaging. Pupil oculography hardware coaxial with the swept-source OCT system enabled fast detection and tracking of the pupil centroid. The pupil tracking ASOCT system with a field of view of 15 x 15 mm achieved diffraction-limited imaging over a lateral tracking range of +/- 2.5 mm and was able to correct eye motion at up to 22 Hz. Pupil tracking ASOCT offers a novel real-time motion compensation approach that may facilitate accurate and reproducible anterior segment imaging.

Optical coherence tomography accurately measures corneal power change from laser refractive surgery

PURPOSE

To determine the ability of motion-corrected optical coherence tomography (OCT) to measure the corneal refractive power change due to LASIK.

DESIGN

Evaluation of a diagnostic test or technology in a cohort.

SUBJECTS

A total of 70 eyes from 37 subjects undergoing LASIK were measured preoperatively. A total of 39 eyes from 22 subjects were measured postoperatively and completed the study.

METHODS

Consecutive patients undergoing LASIK at the Duke Eye Center who consented to participate were imaged with Placido-ring topography, Scheimpflug photography, and OCT on the day of their surgery. Patients were then reimaged with the same imaging systems at the postoperative month 3 visit. Change in preoperative to postoperative corneal refractive power as measured by each of the imaging modalities was compared with the preoperative to postoperative change in manifest refraction (MRx) using the t test with generalized estimating equations.

MAIN OUTCOME MEASURES

Corneal refractive power change due to LASIK as measured by Placido-ring topography, Scheimpflug photography, and OCT compared with the MRx change vertexed to the corneal plane. The change in MRx should correspond to the change in the corneal refractive power from LASIK and was considered the reference measurement.

RESULTS

In 22 individuals (39 eyes) returning after LASIK, we found no significant difference between the clinically measured pre- to post-LASIK change in MRx and both Scheimpflug photography (P = 0.714) and OCT (P = 0.216). In contrast, keratometry values from Placido-ring topography were found to be significantly different from the measured refractive change (P < 0.001). In addition, of the 3 imaging modalities, OCT recorded the smallest mean absolute difference from the reference measurement with the least amount of variability.

CONCLUSIONS

Motion-corrected OCT more accurately measures the change in corneal refractive power due to laser refractive surgery than other currently available clinical devices. By offering accurate corneal refractive power measurements in normal and surgically modified subjects, OCT offers a compelling alternative to current clinical devices for determining corneal refractive power.

Fast acquisition and reconstruction of optical coherence tomography images via sparse representation

In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods.

In situ optical coherence tomography of percutaneous implant-tissue interfaces in a murine model

Novel surface coatings of percutaneous implants need to be tested in biocompatibility studies. The use of animal models for testing usually involves numerous lethal biopsies for the analysis of the implant-tissue interface. In this study, optical coherence tomography (OCT) was used to monitor the reaction of the skin to a percutaneous implant in an animal model of hairless but immunocompetent mice. In vivo optical biopsies with OCT were taken at days 7 and 21 after implantation and post mortem on the day of noticeable inflammation. A Fourier-domain OCT was programmed for spoke pattern scanning schemes centered at the implant midpoint to reduce motion artifacts during in vivo imaging. Image segmentation allowed the automatic detection and morphometric analysis of the skin contour and the subcutaneous implant anchor. On the basis of the segmentation, the overall refractive index of the tissue within one OCT data set was estimated as a free parameter of a fitting algorithm, which corrects for the curved distortion of the planar implant base in the OCT images. OCT in combination with the spoke scanning scheme and image processing provided time-resolved three-dimensional optical biopsies around the implants to assess tissue morphology.

Quantitative single and multi-surface clinical corneal topography utilizing optical coherence tomography

Successful surgical treatment of ocular astigmatism requires accurate characterization of both magnitude and axis of the astigmatism. Keratometry and topography are clinically widely used for this measurement; however, their analysis is limited to the anterior corneal surface. Unlike these techniques, optical coherence tomography (OCT) offers the advantage of measuring both the anterior and posterior corneal surface contributions. We present a technique to combine the local curvatures of both surfaces into a single pseudosurface suitable for clinical application. Building on prior work in distributed scanning OCT (DSOCT) to remove corrupting patient motion artifacts, we present the results of a pilot patient study where extracted values of clinical corneal astigmatic power magnitude and direction from DSOCT corneal volumes were comparable to standard clinical measures of corneal astigmatism.

Efficient sweep buffering in swept source optical coherence tomography using a fast optical switch

We describe a novel buffering technique for increasing the A-scan rate of swept source optical coherence tomography (SSOCT) systems employing low duty cycle swept source lasers. This technique differs from previously reported buffering techniques in that it employs a fast optical switch, capable of switching in 60 ns, instead of a fused fiber coupler at the end of the buffering stage, and is therefore appreciably more power efficient. The use of the switch also eliminates patient exposure to light that is not used for imaging that occurs at the end of the laser sweep, thereby increasing the system sensitivity. We also describe how careful management of polarization can remove undesirable artifacts due to polarization mode dispersion. In addition, we demonstrate how numerical compensation techniques can be used to modify the signal from a Mach-Zehnder interferometer (MZI) clock obtained from the original sweep to recalibrate the buffered sweep, thereby reducing the complexity of systems employing lasers with integrated MZI clocks. Combining these methods, we constructed an SSOCT system employing an Axsun technologies laser with a sweep rate of 100kHz and 6dB imaging range of 5.5mm. The sweep rate was doubled with sweep buffering to 200 kHz, and the imaging depth was extended to 9 mm using coherence revival. We demonstrated the feasibility of this system by acquiring images of the anterior segments and retinas of healthy human volunteers.