Efficiently tracking and imaging interested moving targets is crucial across various applications, from autonomous systems to surveillance. However, persistent challenges remain in various fields, including environmental intricacies, limitations in perceptual technologies, and privacy considerations. We present a teacher-student learning model, the generative adversarial network (GAN)-guided diffractive neural network (DNN), which performs visual tracking and imaging of the interested moving target. The GAN, as a teacher model, empowers efficient acquisition of the skill to differentiate the specific target of interest in the domains of visual tracking and imaging. The DNN-based student model learns to master the skill to differentiate the interested target from the GAN. The process of obtaining a GAN-guided DNN starts with capturing moving objects effectively using an event camera with high temporal resolution and low latency. Then, the generative power of GAN is utilized to generate data with position-tracking capability for the interested moving target, subsequently serving as labels to the training of the DNN. The DNN learns to image the target during training while retaining the target’s positional information. Our experimental demonstration highlights the efficacy of the GAN-guided DNN in visual tracking and imaging of the interested moving target. We expect the GAN-guided DNN can significantly enhance autonomous systems and surveillance.
Undesired fringes can appear in interferometric phase measurement, leading to a degradation of contrast and resolution in the retrieved quantitative phase of measured cells and materials. Strong fringes can also introduce significant phase discontinuities, thereby increasing the complexity and time required for phase unwrapping. These fringes often originate from factors such as light reflection between material surfaces of optical devices. Addressing this issue typically necessitates more intricate optical designs or advanced devices, but this comes at the cost of extended design periods and increased expenses. Achieving complete elimination of these fringes may be not always feasible. In this context, we propose an efficient method to mitigate their influences and enhance object contrast and resolution. This method involves modeling the fringes and appropriately determining their frequency support. Primarily based on Fourier filtering, this approach has been successfully demonstrated using real-world interferometric data.
Two-photon photopolymerization (TPP) has emerged as a popular method for three-dimensional (3D) printing of micro-/nano-structures. On the other hand, optical diffraction tomography (ODT) has become attractive for label-free cell imaging by reconstructing the 3D refractive index (RI) distributions. We propose a high-speed ODT method to fully characterize the TPP-printed structures. Compared with traditional methods like scanning electron microscopy and atomic force microscopy, our ODT-based characterization method offers many advantages, such as revealing both the internal and external morphological features, mapping the 3D RI distributions, and quantifying the surface and side-wall roughness.
Migrasome is a type of recently discovered organelle that plays a vital role in the release of cytosolic contents, regulation of zebrafish embryo formation, mitochondria quality control process, etc. Fluorescence microscopy is widely used to investigate biological specimens, including migrasomes. However, the labelling of fluorescence probes not only requires additional preparation steps, but also may interfere with cellular functions and potentially result in phototoxicity, while only a limited number of labelled structures can be observed at one time. Optical diffraction tomography, as a label-free imaging technique complementary to fluorescence imaging tools, is able to characterize the biophysical properties of organelles. Here we propose to apply optical diffraction tomography for three-dimensional (3D) imaging of migrasome and monitoring its dynamics in living cells.
We have recently demonstrated a high throughput three-dimensional (3D) image flow cytometry method, in which a machine-learning algorithm is used to retrieve the 3D refractive index maps of cells from one angle-multiplexing interferogram. Using this system, we have imaged flowing red blood cells and NIH/3T3 cells with a throughput of more than < 10,000 volumes/second. To further demonstrate its potential on cell phenotyping for clinical testing, we plan to apply this platform to image large populations of various cell types and extracting their morphological and biophysical parameters.
Observation of living plant cells under conventional brightfield microscopy suffers from low imaging contrast. Therefore, fluorescence labeling or fluorescence tag is typically required, but it may not reflect the bona fide state of cellular events and prevent long-term observation due to photobleaching of the fluorescence signal. Therefore, we propose to use quantitative phase imaging (QPI) for label-free imaging of plant cell structures. Using QPI, we have observed vacuoles and nucleus in tobacco BY-2 cells, Arabidopsis cell suspension culture PSB-D, and pollen tubes.
Recent advances in optical diffraction tomography (ODT) have enabled the observation of subcellular structures in living cells through mapping their three-dimensional (3D) refractive index distributions. Illumination angle-scanning based QDT can achieve around 200 nm lateral imaging resolution, however, microscopic structures smaller than 500 nm may not even be clearly observed in living cells. Therefore, we explore the factors that can influence the imaging contrast, including the number of angles used for reconstruction, sample refractive index contrast, and the size of sample features. This study will provide a guidance on optimizing ODT for achieving high contrast cell imaging.
Optical diffraction tomography (ODT) has demonstrated its potential for revealing subcellular structures and quantitative compositions in living cells without chemical staining. Recently, we developed a deep-learning based algorithm to reconstruct the 3D refractive index (RI) maps of cells using a single raw interferogram, measured from an angle-multiplexed ODT system. Using this system, we demonstrated a high throughput 3D image cytometry method, in which a microfluidic chip for controlling cell flow is integrated in the ODT system. By flowing the cells in the chip and minimizing the camera exposure time, we can achieve 3D imaging of over 6,000 cells per second.
A new optical microscopy technique, termed high spatial and temporal resolution synthetic aperture phase microscopy (HISTR-SAPM), is proposed to improve the lateral resolution of wide-field coherent imaging. Under plane wave illumination, the resolution is increased by twofold to around 260 nm, while achieving millisecond-level temporal resolution. In HISTR-SAPM, digital micromirror devices are used to actively change the sample illumination beam angle at high speed with high stability. An off-axis interferometer is used to measure the sample scattered complex fields, which are then processed to reconstruct high-resolution phase images. Using HISTR-SAPM, we are able to map the height profiles of subwavelength photonic structures and resolve the period structures that have 198 nm linewidth and 132 nm gap (i.e., a full pitch of 330 nm). As the reconstruction averages out laser speckle noise while maintaining high temporal resolution, HISTR-SAPM further enables imaging and quantification of nanoscale dynamics of live cells, such as red blood cell membrane fluctuations and subcellular structure dynamics within nucleated cells. We envision that HISTR-SAPM will broadly benefit research in material science and biology.
The development of high throughput three-dimensional (3D) microscopic imaging technique is important for studying cell physiology and early-stage disease diagnoses. Here we propose and demonstrate a digital micromirror device (DMD) based angle-multiplexed high-speed optical diffraction tomography (ODT) technique. Using this ODT technique, we have achieved 3D imaging of cells at over 600 tomogram/second speed, which is 10-100 times faster than current ODTbased 3D cell imaging techniques. We envision that this high-speed ODT system will enable many cutting-edge biomedical applications, such as capturing millisecond scale cell dynamics in 3D space and high throughput 3D imaging of large cell populations.
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