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

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.

Analysis and reduction of noise-induced depolarization in catheter based polarization sensitive optical coherence tomography

In catheter based polarization sensitive optical coherence tomography (PS-OCT), a optical fiber with a rapid rotation in the catheter can cause low signal-to-noise ratio (SNR), polarization state instability, phase change of PS-OCT signals and then heavy noise-induced depolarization, which has a strong impact on the phase retardation measurement of the sample. In this paper, we analyze the noise-induced depolarization and find that the effect of depolarization can be reduced by polar decomposition after incoherent averaging in the Mueller matrix averaging (MMA) method. Namely, MMA can reduce impact of noise on phase retardation mapping. We present a Monte Carlo method based on PS-OCT to numerically describe noise-induced depolarization effect and contrast phase retardation imaging results by MMA and Jones matrix averaging (JMA) methods. The peak signal to noise ratio (PSNR) of simulated images processed by MMA is higher than about 8.9 dB than that processed by JMA. We also implement experiments of multiple biological tissues using the catheter based PS-OCT system. From the simulation and experimental results, we find the polarization contrasts processed by the MMA are better than those by JMA, especially at areas with high depolarization, because the MMA can reduce effect of noise-induced depolarization on the phase retardation measurement.

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.

Improved accuracy of quantitative birefringence imaging by polarization sensitive OCT with simple noise correction and its application to neuroimaging

Polarization-sensitive optical coherence tomography (PS-OCT) enables three-dimensional imaging of biological tissues based on the inherent contrast provided by scattering and polarization properties. In fibrous tissue such as the white matter of the brain, PS-OCT allows quantitative mapping of tissue birefringence. For the popular PS-OCT layout using a single circular input state, birefringence measurements are based on a straight-forward evaluation of phase retardation data. However, the accuracy of these measurements strongly depends on the signal-to-noise ratio (SNR) and is prone to mapping artifacts when the SNR is low. Here we present a simple yet effective approach for improving the accuracy of PS-OCT phase retardation and birefringence measurements. By performing a noise bias correction of the detected OCT signal amplitudes, the impact of the noise floor on retardation measurements can be markedly reduced. We present simulation data to illustrate the influence of the noise bias correction on phase retardation measurements and support our analysis with real-world PS-OCT image data.

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.

Machine-learning based segmentation of the optic nerve head using multi-contrast Jones matrix optical coherence tomography with semi-automatic training dataset generation

A pixel-by-pixel tissue classification framework using multiple contrasts obtained by Jones matrix optical coherence tomography (JM-OCT) is demonstrated. The JM-OCT is an extension of OCT that provides OCT, OCT angiography, birefringence tomography, degree-of-polarization uniformity tomography, and attenuation coefficient tomography, simultaneously. The classification framework consists of feature engineering, k-means clustering that generates a training dataset, training of a tissue classifier using the generated training dataset, and tissue classification by the trained classifier. The feature engineering process generates synthetic features from the primary optical contrasts obtained by JM-OCT. The tissue classification is performed in the feature space of the engineered features. We applied this framework to the in vivo analysis of optic nerve heads of posterior eyes. This classified each JM-OCT pixel into prelamina, lamina cribrosa (lamina beam), and retrolamina tissues. The lamina beam segmentation results were further utilized for birefringence and attenuation coefficient analysis of lamina beam.