Single-shot real-time video recording of a photonic Mach cone induced by a scattered light pulse

Ultrafast video recording of spatiotemporal light distribution in a scattering medium has a significant impact in biomedicine. Although many simulation tools have been implemented to model light propagation in scattering media, existing experimental instruments still lack sufficient imaging speed to record transient light-scattering events in real time. We report single-shot ultrafast video recording of a light-induced photonic Mach cone propagating in an engineered scattering plate assembly. This dynamic light-scattering event was captured in a single camera exposure by lossless-encoding compressed ultrafast photography at 100 billion frames per second. Our experimental results are in excellent agreement with theoretical predictions by time-resolved Monte Carlo simulation. This technology holds great promise for next-generation biomedical imaging instrumentation.

Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model

High-density diffuse optical tomography (HD-DOT) methods have shown significant improvement in localization accuracy and image resolution compared to traditional topographic near infrared spectroscopy of the human brain. In this work we provide a comprehensive evaluation of image quality in visual cortex mapping via a simulation study with the use of an anatomical head model derived from MRI data of a human subject. A model of individual head anatomy provides the surface shape and internal structure that allow for the construction of a more realistic physical model for the forward problem, as well as the use of a structural constraint in the inverse problem. The HD-DOT model utilized here incorporates multiple source-detector separations with continuous-wave data with added noise based on experimental results. To evaluate image quality we quantify the localization error and localized volume at half maximum (LVHM) throughout a region of interest within the visual cortex and systematically analyze the use of whole-brain tissue spatial constraint within image reconstruction. Our results demonstrate that an image quality with less than 10 mm in localization error and 1000 m³ in LVHM can be obtained up to 13 mm below the scalp surface with a typical unconstrained reconstruction and up to 18 mm deep when a whole-brain spatial constraint based on the brain tissue is utilized.

High-Speed Chemical Imaging by Dense-Net Learning of Femtosecond Stimulated Raman Scattering

Hyperspectral stimulated Raman scattering (SRS) by spectral focusing can generate label-free chemical images through temporal scanning of chirped femtosecond pulses. Yet, pulse chirping decreases the pulse peak power and temporal scanning increases the acquisition time, resulting in a much slower imaging speed compared to single-frame SRS using femtosecond pulses. In this paper, we present a deep learning algorithm to solve the inverse problem of getting a chemically labeled image from a single-frame femtosecond SRS image. Our DenseNet-based learning method, termed as DeepChem, achieves high-speed chemical imaging with a large signal level. Speed is improved by 2 orders of magnitude with four subcellular components (lipid droplet, endoplasmic reticulum, nuclei, cytoplasm) classified in MIA PaCa-2 cells and other cell types which were not used for training. Lipid droplet dynamics and cellular response to dithiothreitol in live MIA PaCa-2 cells are demonstrated using this computationally multiplex method.

Modeling and Compensating Temperature-Dependent Non-Uniformity Noise in IR Microbolometer Cameras

Images rendered by uncooled microbolometer-based infrared (IR) cameras are severely degraded by the spatial non-uniformity (NU) noise. The NU noise imposes a fixed-pattern over the true images, and the intensity of the pattern changes with time due to the temperature instability of such cameras. In this paper, we present a novel model and a compensation algorithm for the spatial NU noise and its temperature-dependent variations. The model separates the NU noise into two components: a constant term, which corresponds to a set of NU parameters determining the spatial structure of the noise, and a dynamic term, which scales linearly with the fluctuations of the temperature surrounding the array of microbolometers. We use a black-body radiator and samples of the temperature surrounding the IR array to offline characterize both the constant and the temperature-dependent NU noise parameters. Next, the temperature-dependent variations are estimated online using both a spatially uniform Hammerstein-Wiener estimator and a pixelwise least mean squares (LMS) estimator. We compensate for the NU noise in IR images from two long-wave IR cameras. Results show an excellent NU correction performance and a root mean square error of less than 0.25 ∘C, when the array’s temperature varies by approximately 15 ∘C.

Experimental validation of a high-resolution diffuse optical imaging modality: photomagnetic imaging

We present experimental results that validate our imaging technique termed photomagnetic imaging (PMI). PMI illuminates the medium under investigation with a near-infrared light and measures the induced temperature increase using magnetic resonance imaging. A multiphysics solver combining light and heat propagation is used to model spatiotemporal distribution of temperature increase. Furthermore, a dedicated PMI reconstruction algorithm has been developed to reveal high-resolution optical absorption maps from temperature measurements. Being able to perform measurements at any point within the medium, PMI overcomes the limitations of conventional diffuse optical imaging. We present experimental results obtained on agarose phantoms mimicking biological tissue with inclusions having either different sizes or absorption contrasts, located at various depths. The reconstructed images show that PMI can successfully resolve these inclusions with high resolution and recover their absorption coefficient with high-quantitative accuracy. Even a 1-mm inclusion located 6-mm deep is recovered successfully and its absorption coefficient is underestimated by only 32%. The improved PMI system presented here successfully operates under the maximum skin exposure limits defined by the American National Standards Institute, which opens up the exciting possibility of its future clinical use for diagnostic purposes.

Quasi noise-free digital holography

One of the main drawbacks of Digital Holography (DH) is the coherent nature of the light source, which severely corrupts the quality of holographic reconstructions. Although numerous techniques to reduce noise in DH have provided good results, holographic noise suppression remains a challenging task. We propose a novel framework that combines the concepts of encoding multiple uncorrelated digital holograms, block grouping and collaborative filtering to achieve quasi noise-free DH reconstructions. The optimized joint action of these different image-denoising methods permits the removal of up to 98% of the noise while preserving the image contrast. The resulting quality of the hologram reconstructions is comparable to the quality achievable with non-coherent techniques and far beyond the current state of art in DH. Experimental validation is provided for both single-wavelength and multi-wavelength DH, and a comparison with the most used holographic denoising methods is performed.

A Novel Approach of Parallel Retina-Like Computational Ghost Imaging

Computational ghost imaging (CGI), with the advantages of wide spectrum, low cost, and robustness to light scattering, has been widely used in many applications. The key issue is long time correlations for acceptable imaging quality. To overcome the issue, we propose parallel retina-like computational ghost imaging (PRGI) method to improve the performance of CGI. In the PRGI scheme, sampling and reconstruction are carried out by using the patterns which are divided into blocks from designed retina-like patterns. Then, the reconstructed image of each block is stitched into the entire image corresponding to the object. The simulations demonstrate that the proposed PRGI method can obtain a sharper image while greatly reducing the time cost than CGI based on compressive sensing (CSGI), parallel architecture (PGI), and retina-like structure (RGI), thereby improving the performance of CGI. The proposed method with reasonable structure design and variable selection may lead to improve performance for similar imaging methods and provide a novel technique for real-time imaging applications.

Reducing image artifact in diffuse optical tomography by iterative perturbation correction based on multiwavelength measurements

Ultrasound (US) guided diffuse optical tomography has demonstrated great potential for breast cancer diagnosis, treatment monitoring, and chemotherapy response prediction. Optical measurements of four different wavelengths are used to reconstruct unknown optical absorption maps, which are then used to calculate the hemoglobin concentration distribution of the US visible lesion. Reconstructed absorption maps are prone to image artifacts from outliers in measurement data from tissue heterogeneity, bad coupling between tissue and light guides, and motion by patient or operator. We propose an automated iterative perturbation correction algorithm to reduce image artifacts based on the structural similarity index (SSIM) of absorption maps of four optical wavelengths. The initial image is estimated from the truncated pseudoinverse solution. The SSIM was calculated for each wavelength to assess its similarity with other wavelengths. An absorption map is repeatedly reconstructed and projected back into measurement space to quantify projection error. Outlier measurements with highest projection errors are iteratively removed until all wavelength images are structurally similar with SSIM values greater than a threshold. Clinical data demonstrate statistically significant improvement in image artifact reduction.

Sparse-graph manifold learning method for bioluminescence tomography

In preclinical researches, bioluminescence tomography (BLT) has widely been used for tumor imaging and monitoring, imaged-guided tumor therapy, and so forth. For these biological applications, both tumor spatial location and morphology analysis are the leading problems needed to be taken into account. However, most existing BLT reconstruction methods were proposed for some specific applications with a focus on sparse representation or morphology recovery, respectively. How to design a versatile algorithm that can simultaneously deal with both aspects remains an impending problem. In this study, a Sparse-Graph Manifold Learning (SGML) method was proposed to balance the source sparseness and morphology, by integrating non-convex sparsity constraint and dynamic Laplacian graph model. Furthermore, based on the nonconvex optimization theory and some iterative approximation, we proposed a novel iteratively reweighted soft thresholding algorithm (IRSTA) to solve the SGML model. Numerical simulations and in vivo experiments result demonstrated that the proposed SGML method performed much superior to the comparative methods in spatial localization and tumor morphology recovery for various source settings. It is believed that the SGML method can be applied to the related optical tomography and facilitate the development of optical molecular tomography.

Nonlinear iterative perturbation scheme with simplified spherical harmonics (SP3 ) light propagation model for quantitative photoacoustic tomography

When using quantitative photoacoustic tomography (q-PAT) reconstruction to recover the optical absorption coefficients of tissue, the commonly used diffusion equation has several limitations in the case of the objects that have small geometries and high-absorption or low-scattering areas. Furthermore, the conventional perturbation reconstruction strategy is unsatisfactory when the target tissue containing large heterogeneous features. We herein present a modified q-PAT implementation that employs the higher-order photon migration model achieving the tradeoff between mathematical rigidity and computational efficiency. Besides, a nonlinear iterative method is proposed to obtain the perturbations of optical absorption considering the updating of the sensitivity matrix in calculating the fluence perturbations. Consequently, the distribution of tissue optical properties can be recovered in a robust way even if the targets with high absorption are included. The proposed approach has been validated by simulation, phantom and in vivo experiments, exhibiting promising performances in image fidelity and quantitative feasibility for practical applications.

Parallel-quadrature phase-shifting digital holographic microscopy using polarization beam splitter

We present a digital holography microscopy technique based on parallel-quadrature phase-shifting method. Two π/2 phase-shifted holograms are recorded simultaneously using polarization phase-shifting principle, slightly off-axis recording geometry, and two identical CCD sensors. The parallel phase-shifting is realized by combining circularly polarized object beam with a 45° degree polarized reference beam through a polarizing beam splitter. DC term is eliminated by subtracting the two holograms from each other and the object information is reconstructed after selecting the frequency spectrum of the real image. Both amplitude and phase object reconstruction results are presented. Simultaneous recording eliminates phase errors caused by mechanical vibrations and air turbulences. The slightly off-axis recording geometry with phase-shifting allows a much larger dimension of the spatial filter for reconstruction of the object information. This leads to better reconstruction capability than traditional off-axis holography.

Tutorial on compressed ultrafast photography

Significance

Compressed ultrafast photography (CUP) is currently the world's fastest single-shot imaging technique. Through the integration of compressed sensing and streak imaging, CUP can capture a transient event in a single camera exposure with imaging speeds from thousands to trillions of frames per second, at micrometer-level spatial resolutions, and in broad sensing spectral ranges.

Aim

This tutorial aims to provide a comprehensive review of CUP in its fundamental methods, system implementations, biomedical applications, and prospect.

Approach

A step-by-step guideline to CUP's forward model and representative image reconstruction algorithms is presented with sample codes and illustrations in Matlab and Python. Then, CUP's hardware implementation is described with a focus on the representative techniques, advantages, and limitations of the three key components-the spatial encoder, the temporal shearing unit, and the two-dimensional sensor. Furthermore, four representative biomedical applications enabled by CUP are discussed, followed by the prospect of CUP's technical advancement.

Conclusions

CUP has emerged as a state-of-the-art ultrafast imaging technology. Its advanced imaging ability and versatility contribute to unprecedented observations and new applications in biomedicine. CUP holds great promise in improving technical specifications and facilitating the investigation of biomedical processes.