Point spread function estimation from projected speckle illumination

Randomness assisted in-line holography with deep learning

We propose and demonstrate a holographic imaging scheme exploiting random illuminations for recording hologram and then applying numerical reconstruction and twin image removal. We use an in-line holographic geometry to record the hologram in terms of the second-order correlation and apply the numerical approach to reconstruct the recorded hologram. This strategy helps to reconstruct high-quality quantitative images in comparison to the conventional holography where the hologram is recorded in the intensity rather than the second-order intensity correlation. The twin image issue of the in-line holographic scheme is resolved by an unsupervised deep learning based method using an auto-encoder scheme. Proposed learning technique leverages the main characteristic of autoencoders to perform blind single-shot hologram reconstruction, and this does not require a dataset of samples with available ground truth for training and can reconstruct the hologram solely from the captured sample. Experimental results are presented for two objects, and a comparison of the reconstruction quality is given between the conventional inline holography and the one obtained with the proposed technique.

Intensity correlation scan (IC-scan) technique to characterize the optical nonlinearities of scattering media

Light scattering, whether caused by desired or spurious elements, is considered one of the main phenomena that present great challenges for the nonlinear (NL) optical characterization of turbid media. The most relevant disturbing factor is the random deformation suffered by the spatial intensity distribution of the laser beam due to multiple scattering. In this work, we report the intensity correlation scan (IC-scan) technique as a new tool to characterize the NL optical response of scattering media, by taking advantage of light scattering to generate speckle patterns sensitive to wavefront changes induced by the self-focusing and self-defocusing effects. Peak-to-valley transmittance curves, with a higher signal-to-noise ratio, are obtained by analyzing the spatial intensity correlation functions of the different speckle patterns, even in very turbid media where conventional NL spectroscopy techniques fail. To demonstrate the potential of the IC-scan technique, the NL characterization of colloids that contain a high concentration of silica nanospheres as scatterers, as well as gold nanorods, which act as NL particles and light scatterers, was performed. The results show that the IC-scan technique is more accurate, precise and robust to measure NL refractive indices in turbid media, overcoming limitations imposed by well-established Z-scan and D4σ techniques.

High frame rate multi-perspective cardiac ultrasound imaging using phased array probes

Ultrasound (US) imaging is used to assess cardiac disease by assessing the geometry and function of the heart utilizing its high spatial and temporal resolution. However, because of physical constraints, drawbacks of US include limited field-of-view, refraction, resolution and contrast anisotropy. These issues cannot be resolved when using a single probe. Here, an interleaved multi-perspective 2-D US imaging system was introduced, aiming at improved imaging of the left ventricle (LV) of the heart by acquiring US data from two separate phased array probes simultaneously at a high frame rate. In an ex-vivo experiment of a beating porcine heart, parasternal long-axis and apical views of the left ventricle were acquired using two phased array probes. Interleaved multi-probe US data were acquired at a frame rate of 170 frames per second (FPS) using diverging wave imaging under 11 angles. Image registration and fusion algorithms were developed to align and fuse the US images from two different probes. First- and second-order speckle statistics were computed to characterize the resulting probability distribution function and point spread function of the multi-probe image data. First-order speckle analysis showed less overlap of the histograms (reduction of 34.4%) and higher contrast-to-noise ratio (CNR, increase of 27.3%) between endocardium and myocardium in the fused images. Autocorrelation results showed an improved and more isotropic resolution for the multi-perspective images (single-perspective: 0.59 mm × 0.21 mm, multi-perspective: 0.35 mm × 0.18 mm). Moreover, mean gradient (MG) (increase of 74.4%) and entropy (increase of 23.1%) results indicated that image details of the myocardial tissue can be better observed after fusion. To conclude, interleaved multi-perspective high frame rate US imaging was developed and demonstrated in an ex-vivo experimental setup, revealing enlarged field-of-view, and improved image contrast and resolution of cardiac images.

Fluorescent nanodiamonds for characterization of nonlinear microscopy systems

Characterizing the performance of fluorescence microscopy and nonlinear imaging systems is an essential step required for imaging system optimization and quality control during longitudinal experiments. Emerging multimodal nonlinear imaging techniques require a new generation of microscopy calibration targets that are not susceptible to bleaching and can provide a contrast across the multiple modalities. Here, we present a nanodiamond-based calibration target for microscopy, designed for facilitating reproducible measurements at the object plane. The target is designed to support day-to-day instrumentation development efforts in microscopy laboratories. The images of a phantom contain information about the imaging performance of a microscopy system across multiple spectral windows and modalities. Since fluorescent nanodiamonds are not prone to bleaching, the proposed imaging target can serve as a standard, shelf-stable sample to provide rapid reference measurements for ensuring consistent performance of microscopy systems in microscopy laboratories and imaging facilities.

All-fiber few-mode optical coherence tomography using a modally-specific photonic lantern

Optical coherence tomography (OCT) was recently performed using a few-mode (FM) fiber to increase contrast or improve resolution using a sequential time-domain demultiplexing scheme isolating the different interferometric signals of the mode-coupled backscattered light. Here, we present an all-fiber FM-OCT system based on a parallel modal demultiplexing scheme exploiting a novel modally-specific photonic lantern (MSPL). The MSPL allows for maximal fringe visibility for each fiber propagation mode in an all-fiber assembly which provides the robustness required for clinical applications. The custom-built MSPL was designed for OCT at 930 nm and is wavelength-independent over the broad OCT spectrum. We further present a comprehensive coupling model for the interpretation of FM-OCT images using the first two propagation modes of a few-mode fiber, validate its predictions, and demonstrate the technique using in vitro microbead phantoms and ex vivo biological samples.

A new deep learning method for image deblurring in optical microscopic systems

Deconvolution is the most commonly used image processing method in optical imaging systems to remove the blur caused by the point-spread function (PSF). While this method has been successful in deblurring, it suffers from several disadvantages, such as slow processing time due to multiple iterations required to deblur and suboptimal in cases where the experimental operator chosen to represent PSF is not optimal. In this paper, we present a deep-learning-based deblurring method that is fast and applicable to optical microscopic imaging systems. We tested the robustness of proposed deblurring method on the publicly available data, simulated data and experimental data (including 2D optical microscopic data and 3D photoacoustic microscopic data), which all showed much improved deblurred results compared to deconvolution. We compared our results against several existing deconvolution methods. Our results are better than conventional techniques and do not require multiple iterations or pre-determined experimental operator. Our method has several advantages including simple operation, short time to compute, good deblur results and wide application in all types of optical microscopic imaging systems. The deep learning approach opens up a new path for deblurring and can be applied in various biomedical imaging fields.

An improved Richardson-Lucy iterative algorithm for C-scan image restoration and inclusion size measurement

The accuracy of measuring inclusion size in direct C-scan image of immersion ultrasonic testing is restricted by the lateral resolution of the focused transducer, even if a high frequency is used, and the blurred edge due to scattering of sound waves at inclusions. In this work, an improved image restoration method that is based on the Richardson-Lucy (RL) iterative algorithm is proposed, which is used to restore the C-scan image and improve the accuracy of inclusion size measurement in immersion ultrasonic testing. For the improved RL iterative algorithm, the point spread function (PSF) is derived based on the multi-Gaussian beam model and Kirchhoff approximation, which considers the propagation properties of sound waves at water-steel interface and the spectral characteristics of the transducer with high frequency. In order to determine the final iteration number, the relationship between final iteration number and size of the inclusion in the image is established by restoring the simulated C-scan image and further calibrated with size correction factor. The size correction factor considers the effect of sound attenuation and electro-mechanical transformation encountered in practical testing equipment. Experimental results show that the inclusion sizes measured in restored C-scan images agree well with the optical micrograph results, which prove the effectiveness of the proposed method.

Using speckle to measure tissue dispersion in optical coherence tomography

Tissue dispersion could be used as a marker of early disease changes to further improve the diagnostic potential of optical coherence tomography (OCT). However, most methods to measure dispersion, described in the literature, rely on the presence of distinct and strong reflectors and are, therefore, rarely applicable in vivo. A novel technique has been developed which estimates the dispersion-induced resolution degradation from the image speckle and, as such, is applicable in situ. This method was verified experimentally ex vivo and was applied to the classification of a set of normal and cancerous colon OCT images resulting in 96% correct classification.

Model-based restoration using light vein for range-gated imaging systems

The images captured by an airborne range-gated imaging system are degraded by many factors, such as light scattering, noise, defocus of the optical system, atmospheric disturbances, platform vibrations, and so on. The characteristics of low illumination, few details, and high noise make the state-of-the-art restoration method fail. In this paper, we present a restoration method especially for range-gated imaging systems. The degradation process is divided into two parts: the static part and the dynamic part. For the static part, we establish the physical model of the imaging system according to the laser transmission theory, and estimate the static point spread function (PSF). For the dynamic part, a so-called light vein feature extraction method is presented to estimate the fuzzy parameter of the atmospheric disturbance and platform movement, which make contributions to the dynamic PSF. Finally, combined with the static and dynamic PSF, an iterative updating framework is used to restore the image. Compared with the state-of-the-art methods, the proposed method can effectively suppress ringing artifacts and achieve better performance in a range-gated imaging system.