Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications

Nonmechanical parfocal and autofocus features based on wave propagation distribution in lensfree holographic microscopy

Performing long-term cell observations is a non-trivial task for conventional optical microscopy, since it is usually not compatible with environments of an incubator and its temperature and humidity requirements. Lensless holographic microscopy, being entirely based on semiconductor chips without lenses and without any moving parts, has proven to be a very interesting alternative to conventional microscopy. Here, we report on the integration of a computational parfocal feature, which operates based on wave propagation distribution analysis, to perform a fast autofocusing process. This unique non-mechanical focusing approach was implemented to keep the imaged object staying in-focus during continuous long-term and real-time recordings. A light-emitting diode (LED) combined with pinhole setup was used to realize a point light source, leading to a resolution down to 2.76 μm. Our approach delivers not only in-focus sharp images of dynamic cells, but also three-dimensional (3D) information on their (x, y, z)-positions. System reliability tests were conducted inside a sealed incubator to monitor cultures of three different biological living cells (i.e., MIN6, neuroblastoma (SH-SY5Y), and Prorocentrum minimum). Altogether, this autofocusing framework enables new opportunities for highly integrated microscopic imaging and dynamic tracking of moving objects in harsh environments with large sample areas

Roadmap on digital holography [Invited]

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography

Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing

We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted

Robust deep learning for computational imaging through random optics

Light scattering is a pervasive phenomenon that poses outstanding challenges in both coherent and incoherent imaging systems. The output of a coherent light scattered from a complex medium exhibits a seemingly random speckle pattern that scrambles the useful information of the object. To date, there is no simple solution for inverting such complex scattering. Advancing the solution of inverse scattering problems could provide important insights into applications across many areas, such as deep tissue imaging, non-line-of-sight imaging, and imaging in degraded environment. On the other hand, in incoherent systems, the randomness of scattering medium could be exploited to build lightweight, compact, and low-cost lensless imaging systems that are applicable in miniaturized biomedical and scientific imaging. The imaging capabilities of such computational imaging systems, however, are largely limited by the ill-posed or ill-conditioned inverse problems, which typically causes imaging artifacts and degradation of the image resolution. Therefore, mitigating this issue by developing modern algorithms is essential for pushing the limits of such lensless computational imaging systems. In this thesis, I focus on the problem of imaging through random optics and present two novel deep-learning (DL) based methodologies to overcome the challenges in coherent and incoherent systems: 1) no simple solution for inverse scattering problem and lack of robustness to scattering variations; and 2) ill-posed problem for diffuser-based lensless imaging. In the first part, I demonstrate the novel use of a deep neural network (DNN) to solve the inverse scattering problem in a coherent imaging system. I propose a `one-to-all' deep learning technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. I show for the first time, to the best of my knowledge, that the trained CNN is able to generalize and make high-quality object prediction through an entirely different set of diffusers of the same macroscopic parameter. I then push the limit of robustness against a broader class of perturbations including scatterer change, displacements, and system defocus up to 10X depth of field. In the second part, I consider the utility of the random light scattering to build a diffuser-based computational lensless imaging system and present a generally applicable novel DL framework to achieve fast and noise-robust color image reconstruction. I developed a diffuser-based computational funduscope that reconstructs important clinical features of a model eye. Experimentally, I demonstrated fundus image reconstruction over a large field of view (FOV) and robustness to refractive error using a constant point-spread-function. Next, I present a physics simulator-trained, adaptive DL framework to achieve fast and noise-robust color imaging. The physics simulator incorporates optical system modeling, the simulation of mixed Poisson-Gaussian noise, and color filter array induced artifacts in color sensors. The learning framework includes an adaptive multi-channel L2-regularized inversion module and a channel-attention enhancement network module. Both simulation and experiments show consistently better reconstruction accuracy and robustness to various noise levels under different light conditions compared with traditional L2-regularized reconstructions. Overall, this thesis investigated two major classes of problems in imaging through random optics. In the first part of the thesis, my work explored a novel DL-based approach for solving the inverse scattering problem and paves the way to a scalable and robust deep learning approach to imaging through scattering media. In the second part of the thesis, my work developed a broadly applicable adaptive learning-based framework for ill-conditioned image reconstruction and a physics-based simulation model for computational color imaging

COMPUTATIONAL ANALYSIS OF CODE-MULTIPLEXED COULTER SENSOR SIGNALS

Nowadays, lab-on-a-chip (LoC) technology has been applied in a variety of applications because of its capability to perform accurate microscale manipulations of cells for point-of-care diagnostics. On the other hand, such a result is not readily available from an LoC device and typically still requires a post-inspection of the chip using traditional laboratory equipment such as a microscope, negating the advantages of the LoC technology. To solve this dilemma, my doctoral research mainly focuses on developing portable and disposable biosensors for interfacing with and digitizing the information from an LoC system. Our sensor platform, integrated with multiple microfluidic impedance sensors, electrically monitors and tracks manipulated cells on an LoC device. The sensor platform compresses information from each sensor into a 1-dimensional electrical waveform, and therefore, further signal processing is required to recover the readout of each sensor and extract information of detected cells. Furthermore, with the capability of the sensor platform, we have introduced integrated microfluidic cytometers to characterize properties of cells such as cell surface expression and mechanical properties.Ph.D

Improvements in Digital Holographic Microscopy

The Ph.D. dissertation consists of developing a series of innovative computational methods for improving digital holographic microscopy (DHM). DHM systems are widely used in quantitative phase imaging for studying micrometer-size biological and non-biological samples. As any imaging technique, DHM systems have limitations that reduce their applicability. Current limitations in DHM systems are: i) the number of holograms (more than three holograms) required in slightly off-axis DHM systems to reconstruct the object phase information without applying complex computational algorithms; ii) the lack of an automatic and robust computation algorithm to compensate for the interference angle and reconstruct the object phase information without phase distortions in off-axis DHM systems operating in telecentric and image plane conditions; iii) the necessity of an automatic computational algorithm to simultaneously compensate for the interference angle and numerically focus out-of-focus holograms on reconstructing the object phase information without phase distortions in off-axis DHM systems operating in telecentric regime; iv) the deficiency of reconstructing phase images without phase distortions at video-rate speed in off-axis DHM operating in telecentric regime, and image plane conditions; v) the lack of an open-source library for any DHM optical configuration; and, finally, vi) the tradeoff between speckle contrast and spatial resolution existing in current computational strategies to reduce the speckle contrast. This Ph.D. dissertation is motivated to overcome or at least reduce the six limitations mentioned above. Each chapter of this dissertation presents and discusses a novel computational method from the theoretical and experimental point of view to address each of these limitations