Improving lateral resolution and image quality of optical coherence tomography by the multi-frame superresolution technique for 3D tissue imaging

Super-resolution techniques for biomedical applications and challenges

Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.

True-color and super-resolution imaging using an optical axis shiftable liquid crystal lens

This study explores the utilization of a liquid crystal lens with a shiftable axis for true-color and super-resolution imaging. By maintaining the optical power and shifting the axis of the liquid crystal lens, precise sub-pixel level shifts are applied to the images formed on the sensor, enabling the construction of true-color and super-resolution images. A comparative analysis with the traditional interpolation-based demosaicing method reveals that true-color imaging not only enhances clarity and effective pixel count, but also significantly reduces occurrences of false color, edge aliasing, and color moiré artifacts.

Reflective axicon based energy-efficient extended depth of focus quasi-Bessel beam probe for common-path optical coherence tomography

This work presents an optical fiber negative/reflective axicon probe that generates an energy-efficient quasi-Bessel beam (QBB) having a central spot (CS) possessing ∼20% of the QBB power. With silver coating around the axicon, the CS power has been increased by ∼45%. The QBB possesses a large depth of field, ∼400µm, with a micron order spot size as obtained experimentally. The probe has further been explored for common-path optical coherence tomography. The probe length has been optimized to minimize the path length difference between the reference and sample signal. With a divergence angle of just 0.013°, the beam provides a lateral resolution of ∼2.5 to ∼16µm for an axial distance of 0.1 to 1.0 mm. The imaging results are presented for standard samples such as onion and Scotch tape.

Intravascular optical coherence elastography

Optical coherence elastography (OCE), a functional extension of optical coherence tomography (OCT), visualizes tissue strain to deduce the tissue's biomechanical properties. In this study, we demonstrate intravascular OCE using a 1.1 mm motorized catheter and a 1.6 MHz Fourier domain mode-locked OCT system. We induced an intraluminal pressure change by varying the infusion rate from the proximal end of the catheter. We analysed the pixel-matched phase change between two different frames to yield the radial strain. Imaging experiments were carried out in a phantom and in human coronary arteries in vitro. At an imaging speed of 3019 frames/s, we were able to capture the dynamic strain. Stiff inclusions in the phantom and calcification in atherosclerotic plaques are associated with low strain values and can be distinguished from the surrounding soft material, which exhibits elevated strain. For the first time, circumferential intravascular OCE images are provided side by side with conventional OCT images, simultaneously mapping both the tissue structure and stiffness.

Digital refocusing based on deep learning in optical coherence tomography

We present a deep learning-based digital refocusing approach to extend depth of focus for optical coherence tomography (OCT) in this paper. We built pixel-level registered pairs of en face low-resolution (LR) and high-resolution (HR) OCT images based on experimental data and introduced the receptive field block into the generative adversarial networks to learn the complex mapping relationship between LR-HR image pairs. It was demonstrated by results of phantom and biological samples that the lateral resolutions of OCT images were improved in a large imaging depth clearly. We firmly believe deep learning methods have broad prospects in optimizing OCT imaging.

Convolutional dictionary learning for blind deconvolution of optical coherence tomography images

In this study, we demonstrate a sparsity-regularized, complex, blind deconvolution method for removing sidelobe artefacts and stochastic noise from optical coherence tomography (OCT) images. Our method estimates the complex scattering amplitude of tissue on a line-by-line basis by estimating and deconvolving the complex, one-dimensional axial point spread function (PSF) from measured OCT A-line data. We also present a strategy for employing a sparsity weighting mask to mitigate the loss of speckle brightness within tissue-containing regions caused by the sparse deconvolution. Qualitative and quantitative analyses show that this approach suppresses sidelobe artefacts and background noise better than traditional spectral reshaping techniques, with negligible loss of tissue structure. The technique is particularly useful for emerging OCT applications where OCT images contain strong specular reflections at air-tissue boundaries that create large sidelobe artefacts.

A Tool for High-Resolution Volumetric Optical Coherence Tomography by Compounding Radial-and Linear Acquired B-Scans Using Registration

Optical coherence tomography (OCT) is a medical imaging modality that is commonly used to diagnose retinal diseases. In recent years, linear and radial scanning patterns have been proposed to acquire three-dimensional OCT data. These patterns show differences in A-scan acquisition density across the generated volumes, and thus differ in their suitability for the diagnosis of retinal diseases. While radial OCT volumes exhibit a higher A-scan sampling rate around the scan center, linear scans contain more information in the peripheral scan areas. In this paper, we propose a method to combine a linearly and radially acquired OCT volume to generate a single compound volume, which merges the advantages of both scanning patterns to increase the information that can be gained from the three-dimensional OCT data. We initially generate 3D point clouds of the linearly and radially acquired OCT volumes and use an Iterative Closest Point (ICP) variant to register both volumes. After registration, the compound volume is created by selectively exploiting linear and radial scanning data, depending on the A-scan density of the individual scans. Fusing regions from both volumes with respect to their local A-scan sampling density, we achieve improved overall anatomical OCT information in a high-resolution compound volume. We demonstrate our method on linear and radial OCT volumes for the visualization and analysis of macular holes and the surrounding anatomical structures.

A diagnostic information based framework for super-resolution and quality assessment of retinal OCT images

High-resolution (HR) retinal optical coherence tomography (OCT) images are preferred by the ophthalmologists to diagnose retinal diseases. These images can be obtained by dense scanning of the target retinal region during acquisition. However, a dense scanning increases the image acquisition time and introduces motion artefacts, which corrupt diagnostic information. Therefore, researchers have a growing interest in developing image processing techniques to recover HR images from low-resolution (LR) OCT images. In this paper, we present an automated super-resolution (SR) scheme using diagnostic information weighted sparse representation framework to reconstruct HR images from LR OCT images. The proposed method performs fast and reliable reconstruction of the LR images. We also propose a 2D- variational mode decomposition (VMD) based OCT diagnostic distortion measure (Q_OCT) to quantify diagnostic distortion in the reconstructed OCT images. The SR method is evaluated on clinical grade OCT images with the proposed diagnostic distortion measure along with the conventional non-diagnostic measures like the contrast to noise ratio (CNR), the equivalent number of looks (ENL) and the peak signal to noise ratio (PSNR). The results show an average CNR of 4.07, ENL of 58.96 and PSNR of 27.72 dB. An average score of 1.53 is obtained using the proposed diagnostic distortion measure. Experimental results quantify that the proposed Q_OCT metric can effectively capture diagnostic distortion.

Resolution-enhanced OCT and expanded framework of information capacity and resolution in coherent imaging

Spatial resolution in conventional optical microscopy has traditionally been treated as a fixed parameter of the optical system. Here, we present an approach to enhance transverse resolution in beam-scanned optical coherence tomography (OCT) beyond its aberration-free resolution limit, without any modification to the optical system. Based on the theorem of invariance of information capacity, resolution-enhanced (RE)-OCT navigates the exchange of information between resolution and signal-to-noise ratio (SNR) by exploiting efficient noise suppression via coherent averaging and a simple computational bandwidth expansion procedure. We demonstrate a resolution enhancement of 1.5 × relative to the aberration-free limit while maintaining comparable SNR in silicone phantom. We show that RE-OCT can significantly enhance the visualization of fine microstructural features in collagen gel and ex vivo mouse brain. Beyond RE-OCT, our analysis in the spatial-frequency domain leads to an expanded framework of information capacity and resolution in coherent imaging that contributes new implications to the theory of coherent imaging. RE-OCT can be readily implemented on most OCT systems worldwide, immediately unlocking information that is beyond their current imaging capabilities, and so has the potential for widespread impact in the numerous areas in which OCT is utilized, including the basic sciences and translational medicine.

Model-based optical coherence tomography angiography enables motion-insensitive vascular imaging

We present a significant step toward ultrahigh-resolution, motion-insensitive characterization of vascular dynamics. Optical coherence tomography angiography (OCTA) is an invaluable diagnostic technology for non-invasive, label-free vascular imaging in vivo. However, since it relies on detecting moving cells from consecutive scans, high-resolution OCTA is susceptible to tissue motion, which imposes challenges in resolving and quantifying small vessels. We developed a novel OCTA technique named ultrahigh-resolution factor angiography (URFA) by modeling repeated scans as generative latent variables, with a common variance representing shared features and a unique variance representing motion. By iteratively maximizing the combined log-likelihood probability of these variances, the unique variance is largely separated. Meanwhile, features in the common variance are decoupled, in which vessels with dynamic flow are extracted from tissue structure by integrating high-order factors. Combined with Gabor-domain optical coherence microscopy, URFA successfully extracted high-resolution cutaneous vasculature despite severe involuntary tissue motion and scanner oscillation, significantly improving the visualization and characterization of micro-capillaries in vivo. Compared with the conventional approach, URFA reduces motion artifacts by nearly 50% on average, evaluated on local differences.

Toward image quality assessment in optical coherence tomography (OCT) of rat kidney

BACKGROUND

Optical coherence tomography (OCT) is a useful tool for the evaluation of structure and function of the kidney, but the image quality can be effected by many factors.

OBJECTIVE

The objective of this study was to assess the image quality of different OCT systems in OCT imaging of the living kidney.

METHODS

One swept-source OCT (SSOCT) of 1300 nm, one spectral domain OCT (SDOCT) of 1300 nm and another of 900 nm were used. A FeO phantom was used to establish the point spread function (PSF). Rat kidneys were imaged for image quality assessment. Light penetration in the kidney and the optical attenuation coefficient were also evaluated. The quantification of uriniferous tubules was carried out via the threshold segmentation of 3D OCT images.

RESULTS

The quality of kidney images was resolution dependent. SDOCT of 900 nm showed higher peak signal-to noise ratio and dynamic range. The spatial resolution in the light field could be derived from the PSF distribution along three mutually orthogonal axes. In conjunction with the PSF, the Lucy-Richardson algorithm could improve image quality but could not reveal more microstructural information. The penetration depth of 1300 nm was deeper than that of 900 nm. The attenuation coefficient of the kidney was 29 cm^-1 at 1300 nm and 50 cm^-1 at 900 nm (P < 0.001). More accurate measurement of uriniferous tubules was achieved with the SDOCT-900 due to its higher resolution.

CONCLUSIONS

Both SSOCT and SDOCT systems could be useful for imaging uriniferous tubules in the superficial layers of the cortex. The OCT image quality was highly correlated with the spatial resolution of OCT system.

Optical coherence tomography imaging of plant root growth in soil

Complex interactions between roots and soil provide the nutrients and physical support required for robust plant growth. Yet, visualizing the root-soil interface is challenged by soil's opaque scattering characteristics. Herein, we describe methods for using optical coherence tomography (OCT) to provide non-destructive 3D and cross-sectional root imaging not available with traditional bright-field microscopy. OCT is regularly used for bioimaging, especially in ophthalmology, where it can detect retinal abnormalities. Prior use of OCT in plant biology has focused on surface defects of above-ground tissues, predominantly in food crops. Our results show OCT is also viable for detailed, in situ study of living plant roots. Using OCT for direct observations of root growth in soil can help elucidate key interactions between root morphology and various components of the soil environment including soil structure, microbial communities, and nutrient patches. Better understanding of these interactions can guide efforts to improve plant nutrient acquisition from soil to increase agricultural efficiency as well as better understand drivers of plant growth in natural systems.

Terahertz Spectroscopy and Imaging: A Cutting-Edge Method for Diagnosing Digestive Cancers

The Terahertz's wavelength is located between the microwave and the infrared region of the electromagnetic spectrum. Because it is non-ionizing and non-invasive, Terahertz (THz)-based detection represents a very attractive tool for repeated assessments, patient monitoring, and follow-up. Cancer acts as the second leading cause of death in many regions, and current predictions estimate a continuous increasing trend. Of all types of tumors, digestive cancers represent an important percentage and their incidence is expected to increase more rapidly than other tumor types due to unhealthy lifestyle habits. Because it can precisely differentiate between different types of molecules, depending on water content, the information obtained through THz-based scanning could have several uses in the management of cancer patients and, more importantly, in the early detection of different solid tumors. The purpose of this manuscript is to offer a comprehensive overview of current data available on THz-based detection for digestive cancers. It summarizes the characteristics of THz waves and their interaction with tissues and subsequently presents available THz-based technologies (THz spectroscopy, THz-tomography, and THZ-endoscope) and their potential for future clinical use. The third part of the review is focused on highlighting current in vitro and in vivo research progress in the field, for identifying specific digestive cancers known as oral, esophageal, gastric, colonic, hepatic, and pancreatic tumors.