Maximum a posteriori estimator for high-contrast image composition of optical coherence tomography

Optical coherence tomography image despeckling based on tensor singular value decomposition and fractional edge detection

Optical coherence tomography (OCT) imaging is a technique that is frequently used to diagnose medical conditions. However, coherent noise, sometimes referred to as speckle noise, can dramatically reduce the quality of OCT images, which has an adverse effect on how OCT images are used. In order to enhance the quality of OCT images, a speckle noise reduction technique is developed, and this method is modelled as a low-rank tensor approximation issue. The grouped 3D tensors are first transformed into the transform domain using tensor singular value decomposition (t-SVD). Then, to cut down on speckle noise, transform coefficients are thresholded. Finally, the inverse transform can be used to produce images with speckle suppression. To further enhance the despeckling results, a feature-guided thresholding approach based on fractional edge detection and an adaptive backward projection technique are also presented. Experimental results indicate that the presented algorithm outperforms several comparison methods in relation to speckle suppression, objective metrics, and edge preservation.

Speckle attenuation for optical coherence tomography images using the generalized low rank approximations of matrices

A frequently used technology in medical diagnosis is optical coherence tomography (OCT). However, coherent noise, also known as speckle noise, has the potential to severely reduce the quality of OCT images, which would be detrimental to the use of OCT images for disease diagnosis. In this paper, a despeckling method is proposed to effectively reduce the speckle noise in OCT images using the generalized low rank approximations of matrices (GLRAM). Specifically, the Manhattan distance (MD)-based block matching method is first used to find nonlocal similar blocks for the reference one. The left and right projection matrices shared by these image blocks are then found using the GLRAM approach, and an adaptive method based on asymptotic matrix reconstruction is proposed to determine how many eigenvectors are present in the left and right projection matrices. Finally, all the reconstructed image blocks are aggregated to create the despeckled OCT image. In addition, an edge-guided adaptive back-projection strategy is used to improve the despeckling performance of the proposed method. Experiments with synthetic and real OCT images show that the presented method performs well in both objective measurements and visual evaluation.

Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography

We present deep convolutional neural network (DCNN)-based estimators of the tissue scatterer density (SD), lateral and axial resolutions, signal-to-noise ratio (SNR), and effective number of scatterers (ENS, the number of scatterers within a resolution volume). The estimators analyze the speckle pattern of an optical coherence tomography (OCT) image in estimating these parameters. The DCNN is trained by a large number (1,280,000) of image patches that are fully numerically generated in OCT imaging simulation. Numerical and experimental validations were performed. The numerical validation shows good estimation accuracy as the root mean square errors were 0.23%, 3.65%, 3.58%, 3.79%, and 6.15% for SD, lateral and axial resolutions, SNR, and ENS, respectively. The experimental validation using scattering phantoms (Intralipid emulsion) shows reasonable estimations. Namely, the estimated SDs were proportional to the Intralipid concentrations, and the average estimation errors of lateral and axial resolutions were 1.36% and 0.68%, respectively. The scatterer density estimator was also applied to an in vitro tumor cell spheroid, and a reduction in the scatterer density during cell necrosis was found.

Bayesian analysis of depth resolved OCT attenuation coefficients

Optical coherence tomography (OCT) is an optical technique which allows for volumetric visualization of the internal structures of translucent materials. Additional information can be gained by measuring the rate of signal attenuation in depth. Techniques have been developed to estimate the rate of attenuation on a voxel by voxel basis. This depth resolved attenuation analysis gives insight into tissue structure and organization in a spatially resolved way. However, the presence of speckle in the OCT measurement causes the attenuation coefficient image to contain unrealistic fluctuations and makes the reliability of these images at the voxel level poor. While the distribution of speckle in OCT images has appeared in literature, the resulting voxelwise corruption of the attenuation analysis has not. In this work, the estimated depth resolved attenuation coefficient from OCT data with speckle is shown to be approximately exponentially distributed. After this, a prior distribution for the depth resolved attenuation coefficient is derived for a simple system using statistical mechanics. Finally, given a set of depth resolved estimates which were made from OCT data in the presence of speckle, a posterior probability distribution for the true voxelwise attenuation coefficient is derived and a Bayesian voxelwise estimator for the coefficient is given. These results are demonstrated in simulation and validated experimentally.

Signal averaging improves signal-to-noise in OCT images: But which approach works best, and when?

The high acquisition speed of state-of-the-art optical coherence tomography (OCT) enables massive signal-to-noise ratio (SNR) improvements by signal averaging. Here, we investigate the performance of two commonly used approaches for OCT signal averaging. We present the theoretical SNR performance of (a) computing the average of OCT magnitude data and (b) averaging the complex phasors, and substantiate our findings with simulations and experimentally acquired OCT data. We show that the achieved SNR performance strongly depends on both the SNR of the input signals and the number of averaged signals when the signal bias caused by the noise floor is not accounted for. Therefore we also explore the SNR for the two averaging approaches after correcting for the noise bias and, provided that the phases of the phasors are accurately aligned prior to averaging, then find that complex phasor averaging always leads to higher SNR than magnitude averaging.

Aperture phase modulation with adaptive optics: a novel approach for speckle reduction and structure extraction in optical coherence tomography

Speckle is an inevitable consequence of the use of coherent light in imaging and acts as noise that corrupts image formation in most applications. Optical coherence tomographic imaging, as a technique employing coherence time gating, suffers from speckle. We present here a novel method of suppressing speckle noise intrinsically compatible with adaptive optics (AO) for confocal coherent imaging: modulation of the phase in the system pupil aperture with a segmented deformable mirror (DM) to introduce minor perturbations in the point spread function. This approach creates uncorrelated speckle patterns in a series of images, enabling averaging to suppress speckle noise while maintaining structural detail. A method is presented that efficiently determines the optimal range of modulation of DM segments relative to their AO-optimized position so that speckle noise is reduced while image resolution and signal strength are preserved. The method is active and independent of sample properties. Its effectiveness and efficiency are quantified and demonstrated by both ex vivo non-biological and in vivo biological applications.

Pixel-wise segmentation of severely pathologic retinal pigment epithelium and choroidal stroma using multi-contrast Jones matrix optical coherence tomography

Tissue segmentation of retinal optical coherence tomography (OCT) is widely used in ophthalmic diagnosis. However, its performance in severe pathologic cases is still insufficient. We propose a pixel-wise segmentation method that uses the multi-contrast measurement capability of Jones matrix OCT (JM-OCT). This method is applicable to both normal and pathologic retinal pigment epithelium (RPE) and choroidal stroma. In this method, "features," which are sensitive to specific tissues of interest, are synthesized by combining the multi-contrast images of JM-OCT, including attenuation coefficient, degree-of-polarization-uniformity, and OCT angiography. The tissue segmentation is done by simple thresholding of the feature. Compared with conventional segmentation methods for pathologic maculae, the proposed method is less computationally intensive. The segmentation method was validated by applying it to images from normal and severely pathologic cases. The segmentation results enabled the development of several types of en face visualizations, including melano-layer thickness maps, RPE elevation maps, choroidal thickness maps, and choroidal stromal attenuation coefficient maps. These facilitate close examination of macular pathology. The melano-layer thickness map is very similar to a near infrared fundus autofluorescence image, so the map can be used to identify the source of a hyper-autofluorescent signal.

Noise-bias and polarization-artifact corrected optical coherence tomography by maximum a-posteriori intensity estimation

We propose using maximum a-posteriori (MAP) estimation to improve the image signal-to-noise ratio (SNR) in polarization diversity (PD) optical coherence tomography. PD-detection removes polarization artifacts, which are common when imaging highly birefringent tissue or when using a flexible fiber catheter. However, dividing the probe power to two polarization detection channels inevitably reduces the SNR. Applying MAP estimation to PD-OCT allows for the removal of polarization artifacts while maintaining and improving image SNR. The effectiveness of the MAP-PD method is evaluated by comparing it with MAP-non-PD, intensity averaged PD, and intensity averaged non-PD methods. Evaluation was conducted in vivo with human eyes. The MAP-PD method is found to be optimal, demonstrating high SNR and artifact suppression, especially for highly birefringent tissue, such as the peripapillary sclera. The MAP-PD based attenuation coefficient image also shows better differentiation of attenuation levels than non-MAP attenuation images.