1,721,491 research outputs found
Femtosecond imaging with digital holography
Full text linkWe describe a holographic technique capable of sampling dynamic events at 150 femtosecond time resolution. We apply the technique to the study of the nonlinear propagation of high energy pulses through gas and condensed media. The holograms are recorded as a digitized image from a CCD camera and reconstructed numerically to retrieve the refractive index change during the nonlinear optical process. We show dramatic differences in the pulse propagation characteristics depending on the strength of the nonlinear coefficient of the material and it's time response. Both positive and negative index changes have been measured in different media. The holographic technique allows us to distinguish the very fast positive index changes that are generally attributable to the Kerr nonlinearity from the negative index changes that result from free electrons generated by multiphoton ionization
Mechanically tunable optofluidic distributed feedback dye laser
Full text linkA continuously tunable optofluidic distributed feedback (DFB) dye laser was demonstrated on a monolithic replica molded poly(dimethylsiloxane) (PDMS) chip. The optical feedback was provided by a phase-shifted higher order Bragg grating embedded in the liquid core of a single mode buried channel waveguide. Due to the soft elastomeric nature of PDMS, the laser frequency could be tuned by mechanically stretching the grating period. In principle, the mechanical tuning range is only limited by the gain bandwidth. A tuning range of nearly 60nm was demonstrated from a single dye laser chip by combining two common dye molecules Rhodamine 6G and Rhodamine 101. Single-mode operation was maintained with less than 0.1nm linewidth. Because of the higher order grating, a single laser, when operated with different dye solutions, can provide tunable light output covering the entire spectrum from near UV to near IR in which efficient laser dyes are available. An array of five DFB dye lasers with different grating periods was also demonstrated on a chip. Such tunable integrated laser arrays are expected to become key components in inexpensive advanced spectroscopy chips
Optical detection of asymmetric bacteria utilizing electro orientation
Full text linkWe propose a bacterial detection scheme which uses no biochemical markers and can be applied in a Point-of-Care setting. The detection scheme aligns asymmetric bacteria with an electric field and detects the optical scattering
Optofluidics
Full text link"Optofluidics" is the marriage of optics, optoelectronics and nanophotonics with fluidics. Such integration represents a new approach for dynamic manipulation of optical properties at length scales both greater than and smaller than the wavelength of light with applications ranging from reconfigurable photonic circuits to fluidically adaptable optics to high sensitivity bio-detection currently under development. The capabilities in terms of fluidic control, mixing, miniaturization and optical property tuning afforded by micro-, nano- and electro-fluidics combined with soft lithography based fabrication provides an ideal platform upon which to build such devices. In this paper we provide a general overview of some of the important issues related to the fabrication, integration and operation of optofluidic devices and present three comprehensive application examples: nanofluidically tunable photonic crystals, optofluidic microscopy and DFB dye lasers
A compact optofluidic microscope
Full text linkWe demonstrate a novel optical imaging device that can be directly integrated into a microfluidic network, and therefore enables on-chip imaging in a microfluidic system. This micro imaging device, termed optofluidic microscope (OFM) is free of bulk optics and is based on a nanohole array defined in a non-transmissive metallic layer that is patterned onto the floor of the microfluidic channel. The operation of the optofluidic microscope will be explained in details and its performance is examined by using a popular animal model, Caenorhabditis elegans (C. elegans). Images from a large population of nematode worms are efficiently acquired within a short time frame. The quality of the OFM images of C. elegans and the morphological characteristics revealed therein are evaluated. Two groups of early-stage C. elegans larvae, wild-type and dpy-24 are successfully separated even though their morphological difference at the larval stage is subtle. The experimental results support our claim that the methodology described therein can be effectively used to develop a powerful tool for fulfilling high-resolution, high-throughput imaging task in microfluidics-based systems
Biomedical optical techniques for intracochlear cellular imaging
No full textImaging the inner ear microanatomy is of great importance for the assessment of the state of the cells responsible for sound detection. Hearing disorders are mainly due to a malfunction of the cochlea, the bone containing the cells inside the inner ear. Under this situation, using suitable imaging techniques would be extremely helpful in detecting disease and diagnosing pathologies of the inner ear. However, cochlear small size and encasement in bone provide challenging obstacles, preventing visualization of intracochlear microanatomy using standard clinical imaging modalities.
In this thesis, we explore optical techniques to image inner ear cells and we report an innovative method to observe intracochlear structures through the scattering cochlear bone.
Firstly, we report different imaging techniques we used for intracochlear cellular investigation, starting with conventional optical microscopy. Two-photon excitation fluorescence (TPEF) microscopy is the most suitable technique for high quality images of the hair cells within the organ of Corti. Our results from extracted mouse organ of Corti show that we are able to distinguish between healthy and damaged sensory cells and nerve fibers, analyzing TPEF images from different mouse samples: young, old, exposed to noise and not exposed to noise.
We then describe different noninvasive methods that have been used to image intact cochleae, such as Optical Coherence Tomography (OCT), X-ray Computed Tomography (X-Ray CT), micro-Computed Tomography (X-Ray µCT) and Optical Diffraction Tomography (ODT). These techniques allow the observation of the internal ear anatomy, despite the bone. We report tomographic volumetric reconstructions of mouse cochlea and we compare them in terms of achievable image resolution. µ-CT is the most appropriate techniques providing both penetration through bone and high-level resolution images down to 2µm. However, the high dose of radiation used in µCT studies prevent translation of these techniques to living humans. Due to the discussed challenges for imaging the hair cells through the highly scattering bone, the aim of my thesis is to develop a new method for intracochlear cellular imaging with minimum trauma.
The last chapter represents the core of this PhD project. In this chapter, after considering the advantages and disadvantages of all the possible imaging techniques, we report our innovative technique for visualization of cochlear cells through the overlying scattering bone by combining femtosecond laser bone ablation and TPEF microscopy. The controlled ultrafast laser ablation reduces the optical scattering of the cochlear bone while the TPEF provides a contrast mechanism to resolve individual cells behind the bone. We implemented a simultaneous OCT with the laser ablation to enhance the precision of ablation and prevent inadvertent violation of the delicate cells hidden behind bone. An additional bright-field camera shows real-time images of the sample. We demonstrate that our approach improves the light focusing capability through the cochlear bone, allowing imaging of intracochlear structures in intact cochleae with high resolution.
This extremely highly promising approach can be further developed for clinical ablation instruments and endoscopes that can reach the cochlea and produce images of cells within the inner ear to establish precise diagnosis, guide treatment and assess response to therapies, which is not yet possible in the clinic.L
Advanced Techniques in Optical Diffraction Tomography
No full textIn this thesis, we study the 3 challenges described above. First, we study different reconstruction techniques and assess the fidelity of each reconstruction results by means of structured illumination and phase conjugation. By reconstructing the 3D refractive index of the sample using different algorithms (i.e. Born, Rytov, and Radon) and then perform a numerical back-propagation of experimentally measured structured illumination pattern we are able to assess the fidelity of each reconstruction algorithms without prior information about the 3D RI distribution of the sample.
The second part of the thesis is concerned with the 3D reconstruction of samples using intensity-only measurements which the need to holographically acquire them. We show that using intensity-only measurements, we could still be able to reconstruct the 3D volume of the sample with edge-enhanced effects which was proven useful for drug delivery applications in which nano-particles were identified on the cell membrane of immune T-cells in a drug delivery studies. Such reconstruction technique would result in more robust imaging system where the commercial imaging microscope systems can be incorporated with LEDs for high-quality speckle noise-free imaging systems. In addition, we show that under certain conditions, we can be able to reconstruct the 3D refractive index distribution of different samples.
The third part of the thesis is contributing to high-speed complex wave-front shaping using DMDs. In that part, new modulation technique is demonstrated that can boost the speed of the current time-multiplexing techniques by a factor of 32. The modulation technique is based on amplitude modulation where an amplitude modulator is synchronized with
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the DMD to modulate the intensity of each bit-plane of an 8-bit image and then all the modulated bit-planes are linearly added on the detector. Such modulation technique can be used not only for structured illumination microscopy but also for high-speed 3D printing applications as well as projectors.
The last part is concerned with using deep learning approaches to solve the missing cone problem usually accompanied with optical imaging due to the limited numerical aperture of the imaging system. Two techniques are discussed; the first is based on using a physical model to enhance the quality of the 3D RI reconstruction and the second is based on using deep neural network to solve the missing cone problem.L
3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model
Full text linkOptical tomography has been widely investigated for biomedical imaging applications. In recent years, it has been combined with digital holography and has been employed to produce high quality images of phase objects such as cells. In this Thesis, we look into some of the newest optical Diffraction Tomography (DT) based techniques to solve Three-Dimensional (3D) reconstruction problems and discuss and compare some of the leading ideas and papers. Then we propose a neural-network-based algorithm to solve this problem and apply it on both synthetic and biological samples. Conventional phase tomography with coherent light and off axis recording is performed. The Beam Propagation Method (BPM) is used to model scattering and each x-y plane is modeled by a layer of neurons in the BPM. The network's output (simulated data) is compared to the experimental measurements and the error is used for correcting the weights of the neurons (the refractive indices of the nodes) using standard error back-propagation techniques. The proposed algorithm is detailed and investigated. Then, we look into resolution-conserving regularization and discuss a method for selecting regularizing parameters. In addition, the local minima and phase unwrapping problems are discussed and ways of avoiding them are investigated. It is shown that the proposed learning tomography (LT) achieves better performance than other techniques such as, DT especially when insufficient number or incomplete set of measurements is available. We also explore the role of regularization in obtaining higher fidelity images without losing resolution. It is experimentally shown that due to overcoming multiple scattering, the LT reconstruction greatly outperforms the DT when the sample contains two or more layers of cells or beads. Then, reconstruction using intensity measurements is investigated. 3D reconstruction of a live cell during apoptosis is presented in a time-lapse format. At the end, we present a final comparison with leading papers and commercially available systems. It is shown that -compared to other existing algorithms- the results of the proposed method have better quality. In particular, parasitic granular structures and the missing cone artifact are improved. Overall, the perspectives of our approach are pretty rich for high-resolution tomographic imaging in a range of practical applications.L
Learning approaches to high-fidelity optical diffraction tomography
No full textOptical diffraction tomography (ODT) provides us 3D refractive index (RI) distributions of transparent samples. Since RI values differ across different materials, they serve as endogenous contrasts. It, therefore, enables us to image without pre-processing of labeling which can disturb samples during measurement. It has been utilized in various applications to study hematology, morphological parameters, biochemical information, and so on.
The fundamental principle of ODT reconstruction is to recover the 3D information from multiple 2D measurements. While we require 2D measurements acquired by fully scanning a sample, there exist missing measurements that we are not able to access due to the limited numerical apertures (NAs) in the optical system. This is called the missing cone problem since the parts which are not covered by the NAs form cone shapes. The missing cone problem degrades the final reconstruction by underestimating RI values and more severely elongating images along the optical axis. Another challenge in ODT reconstruction is to model the nonlinear relationship between a sample and the measurements. The first order of scattering is commonly considered while neglecting the other higher orders to linearize the relationship, however, this results in degradation of the final reconstruction as the higher orders of scattering become more pronounced.
In this thesis, we aim at solving the challenges in ODT reconstruction to provide more accurate quantitative information, namely, RI distributions. The first approach is based on model-based iterative reconstruction schemes. We choose the beam propagation method (BPM) for the forward model in order to capture the high orders of scattering. Due to the similarity of the multi-layer structure of the BPM with that of neural networks used in deep learning, we call this scheme learning tomography (LT). We rigorously investigate the performance of LT over the conventional linear model-based reconstruction scheme. Furthermore, by applying a more advanced BPM for the forward model, we even improve the LT and demonstrate the dramatically improved performance by both simulations and experiments.
The second approach is based on statistically learning artifacts present in final reconstructions using a deep neural network (DNN) from a large dataset. Unlike the previous approaches which require iterations, the DNN approach instantly reconstructs RI distributions. We demonstrate the use of DNN using red blood cells which are highly distorted by the missing cone problem. In order to overcome the lack of ground truth in 3D ODT reconstruction, we digitally generate a synthetic dataset. The reconstruction results from the network present highly accurate results for the synthetic test set. Most importantly, we obtain high-fidelity reconstructions of experimental data using the network trained only on the synthetic data.
Unlike other imaging modalities, ODT provides 3D quantitative information without labeling. To fully benefit from the capacity of quantitative imaging, it is critical to solve the existing challenges in ODT reconstruction to produce high-fidelity reconstructions. In this contribution, we aim to resolve the major challenges in ODT reconstruction using various learning approaches, and we believe that it can further improve ODT as a powerful tool for various applications.L
Multiphase flows in Microfluidic Reactors
No full textHydrogen production through water electrolysis is a clean option for storing energy from renewable sources. Low-cost hydrogen production requires efficient water electrolyzers that can produce pure hydrogen at high production throughputs. Membrane-less electrolyzers are promising technologies for hydrogen production due to their simple design, low ionic resistance, and adaptability to various working conditions. The fluidic flow keeps hydrogen and oxygen bubbles from each other in these electrolyzers. Bubbles' motion in these electrolyzers affects product purity and electrochemical performance. Therefore, a thorough understanding of the two-phase flow is needed to improve the performance of membrane-less electrolyzers. In this thesis, we explore the effects of different parameters on the bubbles' trajectory and present methods to enhance the performance of the membrane-less electrolyzers. In the first step, we study the bubble motion in rectangular microchannels. The results indicate that the Reynolds number, Capillary number, Bubble diameter, and channel aspect ratio determine the bubble lateral equilibrium position in the channel. These four parameters together can be modified to control the final equilibrium position of a bubble in a rectangular channel. In the next step, we study the effect of bubble nucleation and interaction on the bubble cross over in membrane-less electrolyzers. We find that the large bubble detachment from the surface of the electrode and the bubble coalescence are two phenomena that lead to the bubble cross over. This problem can be resolved by increasing the flow velocity in order to detach bubbles at a smaller diameter and remove them faster from the channel. However, the energy loss due to the fluidic flow increases with the flow velocity. We showed that the bubbles become smaller without increasing the flow velocity by adding a surfactant to the electrolyte. A surfactant reduces the bubble detachment size and prevents the bubble coalescence. Furthermore, a surfactant can decrease overpotentials by reducing the residence time of bubbles on the surface of the electrode. We use the results of the two-phase flow studies to design the porous wall electrolyzer as a new design for membrane-less electrolyzers. This design is optimized for high throughput production of hydrogen with low cross over. The porous wall electrolyzer has a significantly smaller cross over compared to membrane-less electrolyzers with parallel electrodes. The porous wall electrolyzer operates at the current density of 300 mA/cm^2 with 0.11±0.05% hydrogen cross over to the oxygen side. Finally, we develop a method for the measurement of fluidic properties. This method uses neural networks and images of two-phase flows to measure flow conditions such as flow rate and fluidic properties such as concentration. This measurement technique can be used for controlling two-phase flows in the membrane-less electrolyzers in order to prevent bubble cross over.L
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