We present a new tomographic phase microscopy (TPM) approach that allows capturing the three-dimensional refractive index structure of single cells in suspension without labeling, using 180° rotation of the cells. This is obtained by integrating an external off-axis interferometer for wide-field wave front acquisition with holographic optical tweezers (HOTs) for trapping and micro-rotation of the suspended cells. In contrast to existing TPM approaches for cell imaging, our approach does not require anchoring the sample to a rotating stage, nor is it limited in angular range as is the illumination rotation approach. Thus, it allows noninvasive TPM of suspended live cells in a wide angular range. The proposed technique is experimentally demonstrated by capturing the three-dimensional refractive index map of yeast cells, while collecting interferometric projections at an angular range of 180° with 5° steps. The interferometric projections are processed by both the filtered back-projection method and the diffraction theory method. The experimental system is integrated with a spinning disk confocal fluorescent microscope for validation of the label-free TPM results.

A major challenge in the field of optical imaging of live cells is achieving rapid, 3D, and noninvasive imaging of isolated cells without labeling. If successful, many clinical procedures involving analysis and sorting of cells drawn from body fluids, including blood, can be significantly improved. A new label‐free tomographic interferometry approach is presented. This approach provides rapid capturing of the 3D refractive‐index distribution of single cells in suspension. The cells flow in a microfluidic channel, are trapped, and then rapidly rotated by dielectrophoretic forces in a noninvasive and precise manner. Interferometric projections of the rotated cell are acquired and processed into the cellular 3D refractive‐index map. Uniquely, this approach provides full (360°) coverage of the rotation angular range around any axis, and knowledge on the viewing angle. The experimental demonstrations presented include 3D, label‐free imaging of cancer cells and three types of white blood cells. This approach is expected to be useful for label‐free cell sorting, as well as for detection and monitoring of pathological conditions resulting in cellular morphology changes or occurrence of specific cell types in blood or other body fluids.

We suggest a new implementation for rapid reconstruction of three-dimensional (3-D) refractive index (RI) maps of biological cells acquired by tomographic phase microscopy (TPM). The TPM computational reconstruction process is extremely time consuming, making the analysis of large data sets unreasonably slow and the real-time 3-D visualization of the results impossible. Our implementation uses new phase extraction, phase unwrapping and Fourier slice algorithms, suitable for efficient CPU or GPU implementations. The experimental setup includes an external off-axis interferometric module connected to an inverted microscope illuminated coherently. We used single cell rotation by micro-manipulation to obtain interferometric projections from 73 viewing angles over a 180° angular range. Our parallel algorithms were implemented using Nvidia's CUDA C platform, running on Nvidia's Tesla K20c GPU. This implementation yields, for the first time to our knowledge, a 3-D reconstruction rate higher than video rate of 25 frames per second for 256 × 256-pixel interferograms with 73 different projection angles (64 × 64 × 64 output). This allows us to calculate additional cellular parameters, while still processing faster than video rate. This technique is expected to find uses for real-time 3-D cell visualization and processing, while yielding fast feedback for medical diagnosis and cell sorting.

We present a deep-learning approach for solving the problem of 2π phase ambiguities in two-dimensional quantitative phase maps of biological cells, using a multi-layer encoderdecoder residual convolutional neural network. We test the trained network, PhUn-Net, on various types of biological cells, captured with various interferometric setups, as well as on simulated phantoms. These tests demonstrate the robustness and generality of the network, even for cells of different morphologies or different illumination conditions than PhUn-Net has been trained on. In this paper, for the first time, we make the trained network publicly available in a global format, such that it can be easily deployed on every platform, to yield fast and robust phase unwrapping, not requiring prior knowledge or complex implementation. By this, we expect our phase unwrapping approach to be widely used, substituting conventional and more time-consuming phase unwrapping algorithms.

We present a new phase unwrapping approach, which allows reconstruction of optically thick objects that are optically thin from at least one viewing angle, by considering the information stored in the object phase maps captured from consecutive angles. Our algorithm combines 1-D phase unwrapping in the angular dimension with conventional 2-D phase unwrapping, to achieve unwrapping of the object from the optically thick perspective. We thus obtain quantitative phase imaging of objects that were previously impossible to image in certain viewing angles. To demonstrate our approach, we present both numerical simulation and experimental results for quantitative phase imaging of biological cells.