Two-dimensional phase unwrapping in Doppler Fourier domain optical coherence tomography

High quality of an absolute phase reconstruction for coherent digital holography with an enhanced anti-speckle deep neural unwrapping network

It is always a challenge how to overcome speckle noise interference in the phase reconstruction for coherent digital holography (CDH) and its application, as this issue has not been solved well so far. In this paper, we are proposing an enhanced anti-speckle deep neural unwrapping network (E-ASDNUN) approach to achieve high quality of absolute phase reconstruction for CDH. The method designs a special network-based noise filter and embeds it into a deep neural unwrapping network to enhance anti-noise capacity in the image feature recognition and extraction process. The numerical simulation and experimental test on the phase unwrapping reconstruction and the image quality evaluation under the noise circumstances show that the E-ASDNUN approach is very effective against the speckle noise in realizing the high quality of absolute phase reconstruction. Meanwhile, it also demonstrates much better robustness than the typical U-net neural network and the traditional phase unwrapping algorithms in reconstructing high wrapping densities and high noise levels of phase images. The E-ASDNUN approach is also examined and confirmed by measuring the same phase object using a commercial white light interferometry as a reference. The result is perfectly consistent with that obtained by the E-ASDNUN approach.

1.7-Micron Optical Coherence Tomography Angiography for Characterization of Skin Lesions-A Feasibility Study

Optical coherence tomography (OCT) is a non-invasive diagnostic method that offers real-time visualization of the layered architecture of the skin in vivo. The 1.7-micron OCT system has been applied in cardiology, gynecology and dermatology, demonstrating an improved penetration depth in contrast to conventional 1.3-micron OCT. To further extend the capability, we developed a 1.7-micron OCT/OCT angiography (OCTA) system that allows for visualization of both morphology and microvasculature in the deeper layers of the skin. Using this imaging system, we imaged human skin with different benign lesions and described the corresponding features of both structure and vasculature. The significantly improved imaging depth and additional functional information suggest that the 1.7-micron OCTA system has great potential to advance both dermatological clinical and research settings for characterization of benign and cancerous skin lesions.

A Neural Network Approach to Quantify Blood Flow from Retinal OCT Intensity Time-Series Measurements

Many diseases of the eye are associated with alterations in the retinal vasculature that are possibly preceded by undetected changes in blood flow. In this work, a robust blood flow quantification framework is presented based on optical coherence tomography (OCT) angiography imaging and deep learning. The analysis used a forward signal model to simulate OCT blood flow data for training of a neural network (NN). The NN was combined with pre- and post-processing steps to create an analysis framework for measuring flow rates from individual blood vessels. The framework’s accuracy was validated using both blood flow phantoms and human subject imaging, and across flow speed, vessel angle, hematocrit levels, and signal-to-noise ratio. The reported flow rate of the calibrated NN framework was measured to be largely independent of vessel angle, hematocrit levels, and measurement signal-to-noise ratio. In vivo retinal flow rate measurements were self-consistent across vascular branch points, and approximately followed a predicted power-law dependence on the vessel diameter. The presented OCT-based NN flow rate estimation framework addresses the need for a robust, deployable, and label-free quantitative retinal blood flow mapping technique.

Phase unwrapping based on a residual en-decoder network for phase images in Fourier domain Doppler optical coherence tomography

To solve the phase unwrapping problem for phase images in Fourier domain Doppler optical coherence tomography (DOCT), we propose a deep learning-based residual en-decoder network (REDN) method. In our approach, we reformulate the definition for obtaining the true phase as obtaining an integer multiple of 2π at each pixel by semantic segmentation. The proposed REDN architecture can provide recognition performance with pixel-level accuracy. To address the lack of phase images that are noise and wrapping free from DOCT systems for training, we used simulated images synthesized with DOCT phase image background noise features. An evaluation study on simulated images, DOCT phase images of phantom milk flowing in a plastic tube and a mouse artery, was performed. Meanwhile, a comparison study with recently proposed deep learning-based DeepLabV3+ and PhaseNet methods for signal phase unwrapping and traditional modified networking programming (MNP) method was also performed. Both visual inspection and quantitative metrical evaluation based on accuracy, specificity, sensitivity, root-mean-square-error, total-variation, and processing time demonstrate the robustness, effectiveness and superiority of our method. The proposed REDN method will benefit accurate and fast DOCT phase image-based diagnosis and evaluation when the detected phase is wrapped and will enrich the deep learning-based image processing platform for DOCT images.

Common-path-based device for magnetomotive OCT noise reduction

Magnetomotive optical coherence tomography (MMOCT) is a promising imaging method for noninvasive three-dimensional tracking of magnetic nanoparticle (MNP) motions in target tissues or organs. The external B-field is the driving force that provides MMOCT contrast. However, B-field modulation also introduces modulation noise, thereby decreasing the quality of the MMOCT image. In this paper, a common-path-based device is designed for modulation noise reduction. The device is capable of adjusting interference distance, reference light intensity, and imaging position (X-Y translation). The sensitivity of the MMOCT is increased by ∼20 times with the new device. Using the proposed device, the distribution of MNPs injected in zebrafish was imaged.

Phase unwrapping for Doppler spectral domain optical coherence tomography flow measurement

Doppler optical coherence tomography (OCT) offers additional flow velocity information, which extends the application of OCT. Phase wrapping is the inherent problem that limits measureable range of Doppler OCT. We propose a phase unwrapping method which is suitable for correcting phase in Doppler OCT images. Points (pixels) in flow region are divided into groups according to the radial distance. Points in the same group are supposed to have close velocity. Phase unwrapping algorithm begins at the boundary layer group and is performed sequentially toward the center. Using the proposed criterion, points in a group are separated into two categories, signal points and noise points. Wrapping rounds are determined for signal points phase unwrapping. Mean value of the corrected signal points replaces the noise points for noise reduction. The method is validated with capillary tube flow phantom and in vivo blood flow.

Optimal processing scheme for restoration of phase data corrupted by strong decorrelation noise and dislocations

The presence of speckle noise and dislocations makes phase restoration potentially difficult in quantitative phase imaging and metrology. Unfortunately, there is no appropriate approach to deal with phase data corrupted by high speckle noise and phase dislocations. Usually, processing schemes may deal with low-pass phase filtering, phase unwrapping, or phase inpainting. This paper discusses the efficient processing to deal with noisy phase maps corrupted with phase dislocations. Six processing schemes, combining four operations, are evaluated. The investigation is carried out by realistic numerical simulations in which strong decorrelation phase noise and phase dislocations are generated. As a result, most robust and faster processing is established. The applicability of the optimal scheme is demonstrated through deformation measurement in dental materials.

Advances in Doppler optical coherence tomography and angiography

Since the first demonstration of Doppler optical coherence tomography (OCT) in 1997, several functional extensions of Doppler OCT have been developed, including velocimetry, angiogram, and optical coherence elastography. These functional techniques have been widely used in research and clinical applications, particularly in ophthalmology. Here, we review the principles, representative methods, and applications of different Doppler OCT techniques, followed by discussion on the innovations, limitations, and future directions of each of these techniques.

Computationally effective 2D and 3D fast phase unwrapping algorithms and their applications to Doppler optical coherence tomography

We propose a simplification for a robust and easy to implement fast phase unwrapping (FPU) algorithm that is used to solve the phase wrapping problem encountered in various fields of optical imaging and metrology. We show that the number of necessary computations using the algorithm can be reduced compared to its original version. FPU can be easily extended from two to three spatial dimensions. We demonstrate the applicability of the two- and three-dimensional FPU algorithm for Doppler optical coherence tomography (DOCT) in numerical simulations, and in the imaging of a flow phantom and blood flow in the human retina in vivo. We introduce an FPU applicability plot for use as a guide in the selection of the most suitable version of the algorithm depending on the phase noise in the acquired data. This plot uses the circular standard deviation of the wrapped phase distribution as a measure of noise and relates it to the root-mean-square error of the recovered, unwrapped phase.

High-performance optical coherence velocimeter: theory and applications

We proposed a high-performance optical coherence velocimeter (OCV) based on broadband optical interference which achieves spatial resolution from interference cancellation or enhancement of different components of the broadband light. There is a challengeable issue for OCV that the interference fringes become blurred when the velocity of detected object is relatively large, hindering the pace of OCV application in high-velocity field. To resolve this, the relationship between blurry coefficient and OCV system parameters (e.g., exposure time, central wavelength, bandwidth of source) was derived. It was found that blurry coefficient changed with oscillatory decay form and reached the minimum at each order blurry velocity. It showed that maximum measurable velocity of OCV systems could reach 10th order blurry velocity. The measurement of vibration of the loudspeaker driven by a function signal generator was employed to experimentally verify the velocity measurement performance of the system. The experiment demonstrated that the developed OCV can provide large velocity measurement ranges from static to 25.2 mm/s with nanometer-level precision and maximum measurable vibration frequency of up to 50 kHz. However, in theory, the theoretical maximum measurable velocity can be up to 1.06 m/s for current OCV configuration. The OCV has high precision, large dynamic range, and high-velocity measurement capability, making it attractive for applications in mechanical structure vibration monitoring and acoustic measurement.

Automated phase unwrapping in Doppler optical coherence tomography

Phase wrapping is a crucial issue in Doppler optical coherence tomography (OCT) and restricts its automatic implementation for clinical applications that quantify total retinal blood flow. We propose an automated phase-unwrapping technique that takes advantage of the parabolic profile of blood flow velocity in vessels. Instead of inspecting the phase shift manually, the algorithm calculates the gradient magnitude of the phase shift on the cross-sectional image and automatically detects the presence of phase wrapping. The voxels affected by phase wrapping are corrected according to the determined flow direction adjacent to the vessel walls. We validated this technique in the rodent retina using a prototype visible-light OCT and in the human retina with a commercial infrared OCT system. We believe this signal processing method may well accelerate clinical applications of Doppler OCT in ophthalmology.

Temporally and spatially adaptive Doppler analysis for robust handheld optical coherence elastography

Optical coherence elastography (OCE), a functional extension of optical coherence tomography (OCT), can be used to characterize the mechanical properties of biological tissue. A handheld fiber-optic OCE instrument will allow the clinician to conveniently interrogate the localized mechanical properties of in vivo tissue, leading to better informed clinical decision making. During handheld OCE characterization, the handheld probe is used to compress the sample and the displacement of the sample is quantified by analyzing the OCT signals acquired. However, the motion within the sample inevitably varies in time due to varying hand motion. Moreover, the motion speed depends on spatial location due to the sample deformation. Hence, there is a need for a robust motion tracking method for manual OCE measurement. In this study, we investigate a temporally and spatially adaptive Doppler analysis method. The method described here strategically chooses the time interval (δt) between signals involved in Doppler analysis to track the motion speed v(z,t) that varies temporally and spatially in a deformed sample volume under manual compression. Enabled by temporally and spatially adaptive Doppler analysis, we report the first demonstration of real-time manual OCE characterization of in vivo tissue to the best of our knowledge.