Real-time compressive sensing spectral domain optical coherence tomography

GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer

The image reconstruction for Fourier-domain optical coherence tomography (FD-OCT) could be achieved by iterative methods, which offer a more accurate estimation than the traditional inverse discrete Fourier transform (IDFT) reconstruction. However, the existing iterative methods are mostly A-line-based and are developed on CPU, which causes slow reconstruction. Besides, A-line-based reconstruction makes the iterative methods incompatible with most existing image-level image processing techniques. In this paper, we proposed an iterative method that enables B-scan-based OCT image reconstruction, which has three major advantages: (1) Large-scale parallelism of the OCT dataset is achieved by using GPU acceleration. (2) A novel image-level cross-domain regularizer was developed, such that the image processing could be performed simultaneously during the image reconstruction; an enhanced image could be directly generated from the OCT interferogram. (3) The scalability of the proposed method was demonstrated for 3D OCT image reconstruction. Compared with the state-of-the-art (SOTA) iterative approaches, the proposed method achieves higher image quality with reduced computational time by orders of magnitude. To further show the image enhancement ability, a comparison was conducted between the proposed method and the conventional workflow, in which an IDFT reconstructed OCT image is later processed by a total variation-regularized denoising algorithm. The proposed method can achieve a better performance evaluated by metrics such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), while the speed is improved by more than 30 times. Real-time image reconstruction at more than 20 B-scans per second was realized with a frame size of 4096 (axial) × 1000 (lateral), which showcases the great potential of the proposed method in real-world applications.

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

3-D compressed sensing optical coherence tomography using predictive coding

We present a compressed sensing (CS) algorithm and sampling strategy for reconstructing 3-D Optical Coherence Tomography (OCT) image volumes from as little as 10% of the original data. Reconstruction using the proposed method, Denoising Predictive Coding (DN-PC), is demonstrated for five clinically relevant tissue types including human heart, retina, uterus, breast, and bovine ligament. DN-PC reconstructs the difference between adjacent b-scans in a volume and iteratively applies Gaussian filtering to improve image sparsity. An a-line sampling strategy was developed that can be easily implemented in existing Spectral-Domain OCT systems and reduce scan time by up to 90%.

Restoration Method of Hadamard Coding Spectral Imager

Hadamard coding spectral imaging technology is a computational spectral imaging technology, which modulates the target's spectral information and recovers the original spectrum by inverse transformation. Because it has the advantage of multichannel detection, it is being studied by more researchers. For the engineering realization of push-broom coding spectral imaging instrument, it will inevitably be subjected to push-broom error, template error and detection noise, the redundant sampling problem caused by detector. Therefore, three restoration methods are presented in this paper: firstly, the one is the least squares solution, the two is the zero-filling inverse solution by extending the coding matrix in the redundant coding state to a complete higher order Hadamard matrix, the three is sparse method. Secondly, the numerical and principle analysis shows that the inverse solution of zero-compensation has better robustness and is more suitable for engineering application; its conditional number, error expectation and covariance are better and more stable because it directly uses Hadamard matrix, which has good generalized orthogonality. Then, a real-time spectral reconstruction method is presented, which is based on inverse solution of zero-compensation. Finally, simulation analysis shows that spectral data could be destructed relative accuracy in the error condition; however, the effect of template noise and push error on reconstruction is much greater than that of detection error. Therefore, in addition to reducing the detection noise as much as possible, lower template noise and more accurate push controlling should be guaranteed specifically in engineering realization.

Compressed sensing spectral domain optical coherence tomography with a hardware sparse-sampled camera

We present a sparse-sampled camera for compressed sensing spectral domain optical coherence tomography (CS SD-OCT), which is mainly composed of a novel mask with specially designed coating and a commercially available CCD camera. The sparse-sampled camera under-samples the SD-OCT spectrum in hardware, thus reduces the acquired image data and can achieve faster A-scan speed than conventional CCD camera with the same pixel number. Compared with a conventional SD-OCT system, the CS SD-OCT system equipped with the sparse-sampled camera has better fall-off and SNR performance. CS-OCT imaging of bio-tissue is also demonstrated.

Compressed sensing-enabled phase-sensitive swept-source optical coherence tomography

Here we present a novel phase-sensitive swept-source optical coherence tomography (PhS-SS-OCT) system. The simultaneously recorded calibration signal, which is commonly used in SS-OCT to stabilize the phase, is randomly sub-sampled during the acquisition, and it is later reconstructed based on the Compressed Sensing (CS) theory. We first mathematically investigated the method, and verified it through computer simulations. We then conducted a vibrational frequency test and a flow velocity measurement in phantoms to demonstrate the system's capability of handling phase-sensitive tasks. The proposed scheme shows excellent phase stability with greatly discounted data bandwidth compared with conventional procedures. We further showcased the usefulness of the system in biological samples by detecting the blood flow in ex vivo swine left marginal artery. The proposed system is compatible with most of the existing SS-OCT systems and could be a preferred solution for future high-speed phase-sensitive applications.

Full-depth compressive sensing spectral-domain optical coherence tomography based on a compressive dispersion encoding method

By combining the advantages of compressive sensing optical coherence tomography (OCT) and full-depth OCT in terms of imaging time and imaging depth, we demonstrate how compressive sampling and dispersion encoding can be simultaneously used to reconstruct a full-depth OCT image. Moreover, by considering the image processing speed, we propose a two-step compressive dispersion encoding (TCDE) method, in which a large dispersion imbalance is introduced between the reference arm and the sample arm and two iterations are performed. The first iteration selects the signals with higher intensity and then removes their conjugate items and incoherent aliasing artifacts caused by undersampling based on the least squares method. The second iteration selects the signals with lower intensity. Experimental results show that nearly the same conjugate inhibition ratio can be obtained with 50% sampled data and 100% sampled data using the TCDE method. Full-depth images of a glass slide, an onion, and a live fish eye are obtained from 50% and 100% sampled data using the TCDE method. For a 1.4  mm×3.6  mm fish eye image, the conjugate items are reduced by 33.2 and 31.7 dB using 50% sampled data and 100% sampled data, respectively.

Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images

We demonstrate the usefulness of utilizing a segmentation step for improving the performance of sparsity based image reconstruction algorithms. In specific, we will focus on retinal optical coherence tomography (OCT) reconstruction and propose a novel segmentation based reconstruction framework with sparse representation, termed segmentation based sparse reconstruction (SSR). The SSR method uses automatically segmented retinal layer information to construct layer-specific structural dictionaries. In addition, the SSR method efficiently exploits patch similarities within each segmented layer to enhance the reconstruction performance. Our experimental results on clinical-grade retinal OCT images demonstrate the effectiveness and efficiency of the proposed SSR method for both denoising and interpolation of OCT images.

Shearlet transform: a good candidate for compressed sensing in optical coherence tomography

This paper deals with the development of a fast and smart acquisition technique of Optical Coherence Tomography (OCT) data that has the capability to reconstruct missing data of OCT image. The main objective is to reduce the acquisition time (i.e., increase the frame rate) of an OCT-scan system by choosing a trajectory that covers entirely the image but that does not take all the measurements. The reconstruction of the missing data is achieved by applying an updated Fast Iterative Soft-Thresholding Algorithm (FISTA) on a sparse representation of the image. Several sparse representations have been tested and the shearlets-based approach seems to outperform the other ones (e.g. wavelets, curvelets and by applying bilinear interpolation). The targeted application is a fast OCT imaging solution allowing an efficient compensation of the artefacts induced by the patient physiological motions for diagnostic purpose through optical biopsies (3D micrometric resolution optical images). The obtained results seem promising in terms of low time processing and improvement of the quality of the reconstructed image compared to the traditional sparse acquisition.

Compact high-resolution Littrow conical diffraction spectrometer

This paper presents a compact high-resolution Littrow conical diffraction spectrometer (LCDS) that includes an echelle grating for horizontally dispersing the incident light beam into several high diffraction orders, a prism for vertically separating the overlapping diffraction orders, and a shared focusing lens used for both the incident and dispersed beams. The unique design of the optics enables the LCDS to give high dispersion on the detector without requiring a large field of view and, therefore, to achieve the benefits of high spectral resolution and compactness. The use of the Littrow conical diffraction coupled with the shared focusing lens makes the LCDS more compact. The formulas of the footprint of the dispersed spectra are derived, and the numerical simulation is given. The design calculations for application of the LCDS to an optical coherence tomography system are illustrated by an example.

Iterative l(1)-min algorithm for fixed pattern noise removal in fiber-bundle-based endoscopic imaging

In this study, we developed a signal processing method for fixed pattern noise removal in fiber-bundle-based endoscopic imaging. We physically acquired the fixed pattern of the fiber bundle and used it as a prior image in an l₁ norm minimization (l₁-min) algorithm. We chose an iterative shrinkage thresholding algorithm for l₁ norm minimization. In addition to fixed pattern noise removal, this method also improved image contrast while preserving spatial resolution. The effectiveness of this method was demonstrated on images obtained from a dark-field illuminated reflectance fiber-optic microscope (DRFM). The iterative l₁-min algorithm presented in this paper, in combination with the DRFM system that we previously developed, enables high-resolution, high-sensitivity, intrinsic-contrast, and in situ cellular imaging which has great potential in clinical diagnosis and biomedical research.

Probability-based non-local means filter for speckle noise suppression in optical coherence tomography images

In this Letter, a probability-based non-local means filter is proposed for speckle reduction in optical coherence tomography (OCT). Originally developed for additive white Gaussian noise, the non-local means filter is not suitable for multiplicative speckle noise suppression. This Letter presents a two-stage non-local means algorithm using the uncorrupted probability of each pixel to effectively reduce speckle noise in OCT. Experiments on real OCT images demonstrate that the proposed filter is competitive with other state-of-the-art speckle removal techniques and able to accurately preserve edges and structural details with small computational cost.

3-D Adaptive Sparsity Based Image Compression With Applications to Optical Coherence Tomography

We present a novel general-purpose compression method for tomographic images, termed 3D adaptive sparse representation based compression (3D-ASRC). In this paper, we focus on applications of 3D-ASRC for the compression of ophthalmic 3D optical coherence tomography (OCT) images. The 3D-ASRC algorithm exploits correlations among adjacent OCT images to improve compression performance, yet is sensitive to preserving their differences. Due to the inherent denoising mechanism of the sparsity based 3D-ASRC, the quality of the compressed images are often better than the raw images they are based on. Experiments on clinical-grade retinal OCT images demonstrate the superiority of the proposed 3D-ASRC over other well-known compression methods.

Compressed Sensing for the Fast Computation of Matrices: Application to Molecular Vibrations

This article presents a new method to compute matrices from numerical simulations based on the ideas of sparse sampling and compressed sensing. The method is useful for problems where the determination of the entries of a matrix constitutes the computational bottleneck. We apply this new method to an important problem in computational chemistry: the determination of molecular vibrations from electronic structure calculations, where our results show that the overall scaling of the procedure can be improved in some cases. Moreover, our method provides a general framework for bootstrapping cheap low-accuracy calculations in order to reduce the required number of expensive high-accuracy calculations, resulting in a significant 3× speed-up in actual calculations.

Volumetric (3D) compressive sensing spectral domain optical coherence tomography

In this work, we proposed a novel three-dimensional compressive sensing (CS) approach for spectral domain optical coherence tomography (SD OCT) volumetric image acquisition and reconstruction. Instead of taking a spectral volume whose size is the same as that of the volumetric image, our method uses a sub set of the original spectral volume that is under-sampled in all three dimensions, which reduces the amount of spectral measurements to less than 20% of that required by the Shan-non/Nyquist theory. The 3D image is recovered from the under-sampled spectral data dimension-by-dimension using the proposed three-step CS reconstruction strategy. Experimental results show that our method can significantly reduce the sampling rate required for a volumetric SD OCT image while preserving the image quality.

Real-time dispersion-compensated image reconstruction for compressive sensing spectral domain optical coherence tomography

In this work, we propose a novel dispersion compensation method that enables real-time compressive sensing (CS) spectral domain optical coherence tomography (SD OCT) image reconstruction. We show that dispersion compensation can be incorporated into CS SD OCT by multiplying the dispersion-correcting terms by the undersampled spectral data before CS reconstruction. High-quality SD OCT imaging with dispersion compensation was demonstrated at a speed in excess of 70 frames per s using 40% of the spectral measurements required by the well-known Shannon/Nyquist theory. The data processing and image display were performed on a conventional workstation having three graphics processing units.

Real-time image edge enhancement with a spiral phase filter and graphic processing unit

Isotropic image edge enhancement with high contrast can be achieved using a spiral phase filter (SPF) in a 4f optical system. However, real-time application of edge enhancement with SPF has generally been limited due to the requirement of coherent light or complex phase-shifting operation. In this paper, we demonstrate a real-time image edge enhancement method using a SPF and a graphic processing unit (GPU). By implementing the process of virtual spiral phase filtering on GPU, we are able to speed up the whole procedure by more than 8.3× with respect to CPU processing, and ultimately achieve video rate for megapixel images. In particular, our implementation can achieve higher speedup for more multiple images. These developments are increasing the potential for image edge enhancement of moving objects.

GPU-accelerated non-uniform fast Fourier transform-based compressive sensing spectral domain optical coherence tomography

We implemented the graphics processing unit (GPU) accelerated compressive sensing (CS) non-uniform in k-space spectral domain optical coherence tomography (SD OCT). Kaiser-Bessel (KB) function and Gaussian function are used independently as the convolution kernel in the gridding-based non-uniform fast Fourier transform (NUFFT) algorithm with different oversampling ratios and kernel widths. Our implementation is compared with the GPU-accelerated modified non-uniform discrete Fourier transform (MNUDFT) matrix-based CS SD OCT and the GPU-accelerated fast Fourier transform (FFT)-based CS SD OCT. It was found that our implementation has comparable performance to the GPU-accelerated MNUDFT-based CS SD OCT in terms of image quality while providing more than 5 times speed enhancement. When compared to the GPU-accelerated FFT based-CS SD OCT, it shows smaller background noise and less side lobes while eliminating the need for the cumbersome k-space grid filling and the k-linear calibration procedure. Finally, we demonstrated that by using a conventional desktop computer architecture having three GPUs, real-time B-mode imaging can be obtained in excess of 30 fps for the GPU-accelerated NUFFT based CS SD OCT with frame size 2048(axial) × 1,000(lateral).