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We present a lens-free optical tomographic microscope, which enables imaging a large volume of approximately 15 mm³ on a chip, with a spatial resolution of < 1 μm x < 1 μm x < 3 μm in x, y and z dimensions, respectively. In this lens-free tomography modality, the sample is placed directly on a digital sensor array with, e.g., [less-than or equal to] 4 mm distance to its active area. A partially coherent light source placed approximately 70 mm away from the sensor is employed to record lens-free in-line holograms of the sample from different viewing angles. At each illumination angle, multiple subpixel shifted holograms are also recorded, which are digitally processed using a pixel superresolution technique to create a single high-resolution hologram of each angular projection of the object. These superresolved holograms are digitally reconstructed for an angular range of ± 50°, which are then back-projected to compute tomograms of the sample. In order to minimize the artifacts due to limited angular range of tilted illumination, a dual-axis tomography scheme is adopted, where the light source is rotated along two orthogonal axes. Tomographic imaging performance is quantified using microbeads of different dimensions, as well as by imaging wild-type Caenorhabditis elegans. Probing a large volume with a decent 3D spatial resolution, this lens-free optical tomography platform on a chip could provide a powerful tool for high-throughput imaging applications in, e.g., cell and developmental biology.

Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 μm have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of ~93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency–approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality. Air-quality monitoring: lens-free microscopy system Accurate on-site air-quality monitoring can be performed using lens-free microscopy on a chip coupled with machine learning. To monitor and enhance air quality, it is vital to realize rapid, accurate and high-throughput sizing of airborne particles. A portable system built by Aydogan Ozcan and co-workers from the University of California, Los Angeles, generates statistics of particle size and density from microscopic images of particulate matter in air. A sticky coverslip captures airborne particles and then light from three LEDs (red, green and blue) creates holograms of the particle distribution, captured on a CMOS image sensor and processed. The system can screen 6.5 litres of air in about 30 s and has a particle sizing accuracy of about 93%. Results obtained using this technology achieved a strong correlation with those acquired using conventional particle-sizing devices.

Routine antimicrobial susceptibility testing (AST) can prevent deaths due to bacteria and reduce the spread of multi-drug-resistance, but cannot be regularly performed in resource-limited-settings due to technological challenges, high-costs, and lack of trained professionals. We demonstrate an automated and cost-effective cellphone-based 96-well microtiter-plate (MTP) reader, capable of performing AST without the need for trained diagnosticians. Our system includes a 3D-printed smartphone attachment that holds and illuminates the MTP using a light-emitting-diode array. An inexpensive optical fiber-array enables the capture of the transmitted light of each well through the smartphone camera. A custom-designed application sends the captured image to a server to automatically determine well-turbidity, with results returned to the smartphone in ~1 minute. We tested this mobile-reader using MTPs prepared with 17 antibiotics targeting Gram-negative bacteria on clinical isolates of Klebsiella pneumoniae, containing highly-resistant antimicrobial profiles. Using 78 patient isolate test-plates, we demonstrated that our mobile-reader meets the FDA-defined AST criteria, with a well-turbidity detection accuracy of 98.21%, minimum-inhibitory-concentration accuracy of 95.12%, and a drug-susceptibility interpretation accuracy of 99.23%, with no very major errors. This mobile-reader could eliminate the need for trained diagnosticians to perform AST, reduce the cost-barrier for routine testing, and assist in spatio-temporal tracking of bacterial resistance.

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional.

In digital tomosynthesis imaging, multiple projections of an object are obtained along a small range of different incident angles in order to reconstruct a pseudo-3D representation of the object. In this paper we discuss a mathematical model for polyenergetic digital breast tomosynthesis image reconstruction that explicitly takes into account various materials composing the object and the polyenergetic nature of the x-ray beam. Our model allows for computing weight fractions of the individual materials that make up the object, which can then be used to reconstruct pseudo-3D images. The reconstruction process requires solving a large-scale inverse problem, which is done with a gradient descent iteration. Regularization is enforced by truncating the iteration. The mathematical model is described in detail, as is an efficient approach to compute the gradient of the objective function. The effectiveness of our approach is illustrated with real data taken of an object with known materials that simulates an actual breast. [PUBLICATION ABSTRACT]

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing 'slide-level' diagnosis by using individual 'cell-level' diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.

is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved a limit of detection of 12 cysts per 10 ml, an average cyst capture efficiency of ~79%, and an accuracy of ~95%. Providing rapid detection and quantification of waterborne pathogens without the need for a microbiology expert, this field-portable imaging and sensing platform running on a smartphone could be very useful for water quality monitoring in resource-limited settings.

Current contaminant and residue monitoring throughout the food chain is based on sampling, transport, administration, and analysis in specialized control laboratories. This is a highly inefficient and costly process since typically more than 99 % of the samples are found to be compliant. On-site simplified prescreening may provide a scenario in which only samples that are suspect are transported and further processed. Such a prescreening can be performed using a small attachment on a cellphone. To this end, a cellphone-based imaging platform for a microsphere fluorescence immunoassay that detects the presence of anti-recombinant bovine somatotropin (rbST) antibodies in milk extracts was developed. RbST administration to cows increases their milk production, but is illegal in the EU and a public health concern in the USA. The cellphone monitors the presence of anti-rbST antibodies (rbST biomarker), which are endogenously produced upon administration of rbST and excreted in milk. The rbST biomarker present in milk extracts was captured by rbST covalently coupled to paramagnetic microspheres and labeled by quantum dot (QD)-coupled detection antibodies. The emitted fluorescence light from these captured QDs was then imaged using the cellphone camera. Additionally, a dark-field image was taken in which all microspheres present were visible. The fluorescence and dark-field microimages were analyzed using a custom-developed Android application running on the same cellphone. With this setup, the microsphere fluorescence immunoassay and cellphone-based detection were successfully applied to milk sample extracts from rbST-treated and untreated cows. An 80 % true-positive rate and 95 % true-negative rate were achieved using this setup. Next, the cellphone-based detection platform was benchmarked against a newly developed planar imaging array alternative and found to be equally performing versus the much more sophisticated alternative. Using cellphone-based on-site analysis in future residue monitoring can limit the number of samples for laboratory analysis already at an early stage. Therewith, the entire monitoring process can become much more efficient and economical. Figure Cellphone-based detection platform for rbST biomarker analysis in milk extracts using a microsphere fluorescence immunoassay

A novel imaging technology, digital breast tomosynthesis (DBT), is a technique that overcomes the tissue superposition limitation of conventional mammography by acquiring a limited number of X-ray projections from a narrow angular range, and combining these projections to reconstruct a pseudo-3D image. The emergence of DBT as a potential replacement or adjunct to mammographic screening mandates that solutions be found to two of its major limitations, namely X-ray scatter and mono-energetic reconstruction methods. A multi-faceted software-based approach to enhance the image quality of DBT imaging has the potential to increase the sensitivity and specificity of breast cancer detection and diagnosis. A scatter correction (SC) algorithm and a spectral reconstruction (SR) algorithm are both ready for implementation and clinical evaluation in a DBT system and exhibit the potential to improve image quality. A principal component analysis (PCA) based model of breast shape and a PCA model of X-ray scatter optimize the SC algorithm for the clinical realm. In addition, a comprehensive dosimetric characterization of a FDA approved DBT system has also been performed, and the feasibility of a new dual-spectrum, single-acquisition DBT imaging technique has also been evaluated.

This research tests for the first time in-vivo a data reduction scheme based on a modified integrate-and-fire pulse encoding for an implanted neural recording system in wireless transmission applications. Wireless transmission from implanted multi-channel recordings imposes many constraints on the system but the major constraint is bandwidth. Other constraints such as large dynamic range, low power consumption, small device size and noise robustness, are serious but can be more easily met. This neural recording system consists of a front-end hardware recording part and a back-end signal processing part. The integrate-and-fire (IF) mechanism is adopted in the analog front-end circuit design to achieve low bandwidth for data compression. The neural signal is encoded and transformed into a pulse representation. The encoded pulse representation is inherently noise robust and beneficial in wireless transmission for the signal processing in the digital back-end system. As a result, a traditional analog-to-digital converter (ADC), is not required in this neural recording application. In the digital back-end part, the system can either reconstruct the recorded pulses back to a traditional sampled continuous-time signal, and then sort neural spikes upon the reconstructed signals, or directly execute the pulse-based spike sorting algorithm in the pulse domain, even when the maximum inter-pulse interval (IPI) of the encoded pulses is in sub-Nyquist regions. In this research, we first successfully record action potentials via the UF system adopting the IF neuron circuit in the in-vivo recording. To conduct an in-vivo recording, the implanted electrode must be well placed to detect available neural signals, and the analog and digital parts of the UF system need parameter optimization and calibration. In addition, the dual system experiment, comprising the UF system and the TDT system, verifies that the UF recording system extracts quality in-vivo signals. In an in-vivo recording, the spike sorting results for these recording systems classify the same neural signals. The UF system can record 1000 μVpp high action potential signals but induces about 3.6 dB higher noise levels than the TDT recording system does. The SNR of the UF system is about 11.43 dB with a pulse rate less than 30 Kpulses/sec while the SNR of the TDT system ranges is about 15.03 dB with a bandwidth of 400 Kbits/sec. The trade-off of SNR and recording bandwidth is observed. Although the sorted spikes in the reconstructed signal are distorted, the distortion is constant throughout the recording and the error does not influence the neural signal classification. This experiment shows that the decrease of the SNR does not influence the spike sorting result. The modified UF system can reduce the wireless transmission bandwidth via three versatile neuron circuit strategies: the adaptive, leaky and refractory components of the neuron circuit form the adaptive leaky refractory integrate-and-fire (ALRIF) neuron circuit. MATLAB simulation results for all these neuron circuit models show a proof of concept. The refractory neuron circuit limits the maximum peak data bandwidth. The leaky neuron circuit filters out high-frequency noise, which further reduces bandwidth. The adaptive neuron circuit achieves more than 40% data compression compared to the simple IF neuron circuit in simulation. The idea of the adaptive neuron circuit is novel in the integration with a simple IF neuron circuit and unique for reconstruction purposes. The design, fabrication and test, of the adaptive neuron circuit are presented. Simulation of the complete ALRIF neuron circuit illustrates its performance by showing three distinctly sorted spikes in a neural simulator test.