Application of an Alternating Minimization Algorithm to Experimental DIC Microscopy Data for the Quantitative Determination of Sample Optical Properties

Differential Interference Contrast (DIC) is commonly chosen for imaging unstained transparent samples. One limitation of DIC microscopy is the qualitative results it provides. This must be post-processed to extract meaningful information. The Alternating Minimizatio (AM) algorithm studied in this thesis is an iterative approach to recover a quantitative estimate of a sample\u27s complex-valued transmittance function. The AM algorithm is validated using simulated data. Additionally, the bias retardation and shear distance, two characteristic features of the DIC system, must be measured to insure the system model is accurate. This is accomplished by introducing a calibrated liquid crystal device to the system. Algorithm performance is verified using an experimental test object before finally being applied to biological samples. Overall results demonstrate the accuracy of this algorithm\u27s object estimation results. These are verified through comparison to similar data processing techniques

Mapping optical path length and image enhancement using quantitative orientation-independent differential interference contrast microscopy

Author Posting. © Society of Photo Optical Instrumentation Engineers, 2017. This article is posted here by permission of Society of Photo Optical Instrumentation Engineers for personal use, not for redistribution. The definitive version was published in Journal of Biomedical Optics 22 (2017): 016006, doi:10.1117/1.JBO.22.1.016006.We describe the principles of using orientation-independent differential interference contrast (OI-DIC) microscopy for mapping optical path length (OPL). Computation of the scalar two-dimensional OPL map is based on an experimentally received map of the OPL gradient vector field. Two methods of contrast enhancement for the OPL image, which reveal hardly visible structures and organelles, are presented. The results obtained can be used for reconstruction of a volume image. We have confirmed that a standard research grade light microscope equipped with the OI-DIC and 100×/1.3 NA objective lens, which was not specially selected for minimum wavefront and polarization aberrations, provides OPL noise level of ∼0.5  nm and lateral resolution if ∼300  nm at a wavelength of 546 nm. The new technology is the next step in the development of the DIC microscopy. It can replace standard DIC prisms on existing commercial microscope systems without modification. This will allow biological researchers that already have microscopy setups to expand the performance of their systems.This publication was made possible by Grant No. R01-GM101701 from the National Institute of General Medical Sciences, National Institutes of Health

Visualizing Escherichia coli Sub-Cellular Structure Using Sparse Deconvolution Spatial Light Interference Tomography

Studying the 3D sub-cellular structure of living cells is essential to our understanding of biological function. However, tomographic imaging of live cells is challenging mainly because they are transparent, i.e., weakly scattering structures. Therefore, this type of imaging has been implemented largely using fluorescence techniques. While confocal fluorescence imaging is a common approach to achieve sectioning, it requires fluorescence probes that are often harmful to the living specimen. On the other hand, by using the intrinsic contrast of the structures it is possible to study living cells in a non-invasive manner. One method that provides high-resolution quantitative information about nanoscale structures is a broadband interferometric technique known as Spatial Light Interference Microscopy (SLIM). In addition to rendering quantitative phase information, when combined with a high numerical aperture objective, SLIM also provides excellent depth sectioning capabilities. However, like in all linear optical systems, SLIM's resolution is limited by diffraction. Here we present a novel 3D field deconvolution algorithm that exploits the sparsity of phase images and renders images with resolution beyond the diffraction limit. We employ this label-free method, called deconvolution Spatial Light Interference Tomography (dSLIT), to visualize coiled sub-cellular structures in E. coli cells which are most likely the cytoskeletal MreB protein and the division site regulating MinCDE proteins. Previously these structures have only been observed using specialized strains and plasmids and fluorescence techniques. Our results indicate that dSLIT can be employed to study such structures in a practical and non-invasive manner

Computational optical imaging: Applications in synthetic aperture imaging, phase retrieval, and digital holography

Computational imaging has become an important field, as a merger of both algorithms and physical experiments. In the realm of microscopy and optical imaging, an important application is the problem of improving resolution, which is bounded by wavelength and numerical aperture according to the classic diffraction limit. We will investigate the resolution enhancement of phase objects such as transparent biological cells. One key challenge is how to measure phase experimentally. Standard interferometric techniques have the drawback of being sensitive to environmental vibrations and temperature fluctuations, and they use a reference arm which requires more space and cost. Non-holographic methods provide a way to overcome these disadvantages. Another challenge is how to reconstruct phase and amplitude from a digital hologram. The typical method of applying a filter in the frequency domain is limited by finite filter size. Optimization approaches offer a solution to this problem. The work presented here spans three main aspects of phase imaging microscopy including synthetic aperture imaging, phase retrieval, and digital holography. We develop a non-holographic microscope that uses off-axis illumination for resolution enhancement and demonstrate the first experimental measurements of referenceless phase retrieval at multiple angles. We implement a synthetic aperture microscope using an electrically tunable lens to defocus images, which avoids the need to mechanically move a camera on a translation stage. Finally, we improve the reconstruction of images from a digital hologram based on an iterative algorithm that alternatively updates amplitude and phase

Double Alternating Minimization (DAM) for Phase Retrieval in the Presence of Poisson Noise and Pixelation

Optical detectors, such as photodiodes and CMOS cameras, can only read intensity information, and thus phase information of wavefronts is lost. Phase retrieval algorithms are used to estimate the lost phase and reconstruct an accurate effective pupil function, where the squared modulus of its Fourier transform is detected by a camera. However, current algorithms such as the Gerchberg-Saxton algorithm and Fienup-style algorithm do not consider the detector sampling rate and shot noise introduced by photon detection. If the sampling rate is low, we must interpolate the detected image in order to accurately reconstruct its pupil function. Here, we develop an appropriate estimation method for interpolating the detected image by using penalized I-divergence and then use the interpolated image for phase retrieval. In our simulation, after 300 iterations of our DAM algorithm, the phase-retrieved pupil function has a root-mean-squared error of about 43±3% less than Fienup-style algorithm with nearest neighbor interpolation when one hundred million photons are collected

Automated characterization of cell shape changes during amoeboid motility by skeletonization

<p>Abstract</p> <p>Background</p> <p>The ability of a cell to change shape is crucial for the proper function of many cellular processes, including cell migration. One type of cell migration, referred to as amoeboid motility, involves alternating cycles of morphological expansion and retraction. Traditionally, this process has been characterized by a number of parameters providing global information about shape changes, which are insufficient to distinguish phenotypes based on local pseudopodial activities that typify amoeboid motility.</p> <p>Results</p> <p>We developed a method that automatically detects and characterizes pseudopodial behavior of cells. The method uses skeletonization, a technique from morphological image processing to reduce a shape into a series of connected lines. It involves a series of automatic algorithms including image segmentation, boundary smoothing, skeletonization and branch pruning, and takes into account the cell shape changes between successive frames to detect protrusion and retraction activities. In addition, the activities are clustered into different groups, each representing the protruding and retracting history of an individual pseudopod.</p> <p>Conclusions</p> <p>We illustrate the algorithms on movies of chemotaxing <it>Dictyostelium </it>cells and show that our method makes it possible to capture the spatial and temporal dynamics as well as the stochastic features of the pseudopodial behavior. Thus, the method provides a powerful tool for investigating amoeboid motility.</p

Efficient Computing for Three-Dimensional Quantitative Phase Imaging

Quantitative Phase Imaging (QPI) is a powerful imaging technique for measuring the refractive index distribution of transparent objects such as biological cells and optical fibers. The quantitative, non-invasive approach of QPI provides preeminent advantages in biomedical applications and the characterization of optical fibers. Tomographic Deconvolution Phase Microscopy (TDPM) is a promising 3D QPI method that combines diffraction tomography, deconvolution, and through-focal scanning with object rotation to achieve isotropic spatial resolution. However, due to the large data size, 3D TDPM has a drawback in that it requires extensive computation power and time. In order to overcome this shortcoming, CPU/GPU parallel computing and application-specific embedded systems can be utilized. In this research, OpenMP Tasking and CUDA Streaming with Unified Memory (TSUM) is proposed to speed up the tomographic angle computations in 3D TDPM. TSUM leverages CPU multithreading and GPU computing on a System on a Chip (SoC) with unified memory. Unified memory eliminates data transfer between CPU and GPU memories, which is a major bottleneck in GPU computing. This research presents a speedup of 3D TDPM with TSUM for a large dataset and demonstrates the potential of TSUM in realizing real-time 3D TDPM.M.S

Beating Nyquist with Ultrafast Optical Pulses

Photonic sources readily provide several THz of analog bandwidth for information processing. Taking advantage of this fact, problems such as ultrawideband radio (RF) spectrum sensing, high performance radar, and analog-to-digital conversion can achieve significant performance gains with photonic techniques. Likewise, photonic imaging systems such as time-stretch microscopy have produced a breakthrough in continuous high speed imaging, enabling faster shutter speeds, higher frame rates, and greater gain-bandwidth product than is possible with continuous read-out CCDs and CMOS sensor arrays. However, imaging at this rate with traditional Nyquist sampling inevitably yields sustained data output on the order of 100 Gb/s or more, creating a significant challenge for storage and transmission. Real images and video are highly compressible, so this deluge of data is also highly inefficient. This thesis will address several techniques based on chirp-processing of ultrafast laser pulses that demonstrate real-time efficient compression of both electronic and optical signals, overcoming electronic bottlenecks via optical processing in the analog domain. Several systems will also be presented that permit greater information extraction from high throughput microscopy experiments by measuring quantitative phase images on a time-stretch microscope

PhaseMax: Convex Phase Retrieval via Basis Pursuit

We consider the recovery of a (real- or complex-valued) signal from magnitude-only measurements, known as phase retrieval. We formulate phase retrieval as a convex optimization problem, which we call PhaseMax. Unlike other convex methods that use semidefinite relaxation and lift the phase retrieval problem to a higher dimension, PhaseMax is a "non-lifting" relaxation that operates in the original signal dimension. We show that the dual problem to PhaseMax is Basis Pursuit, which implies that phase retrieval can be performed using algorithms initially designed for sparse signal recovery. We develop sharp lower bounds on the success probability of PhaseMax for a broad range of random measurement ensembles, and we analyze the impact of measurement noise on the solution accuracy. We use numerical results to demonstrate the accuracy of our recovery guarantees, and we showcase the efficacy and limits of PhaseMax in practice