Automated, Depth-Resolved Estimation of the Attenuation Coefficient From Optical Coherence Tomography Data.

We present a method for automated, depth-resolved extraction of the attenuation coefficient from Optical Coherence Tomography (OCT) data. In contrast to previous automated, depth-resolved methods, the Depth-Resolved Confocal (DRC) technique derives an invertible mapping between the measured OCT intensity data and the attenuation coefficient while considering the confocal function and sensitivity fall-off, which are critical to ensure accurate measurements of the attenuation coefficient in practical settings (e.g., clinical endoscopy). We also show that further improvement of the estimated attenuation coefficient is possible by formulating image denoising as a convex optimization problem that we term Intensity Weighted Horizontal Total Variation (iwhTV). The performance and accuracy of DRC alone and DRC+iwhTV are validated with simulated data, optical phantoms, and ex-vivo porcine tissue. Our results suggest that implementation of DRC+iwhTV represents a novel way to improve OCT contrast for better tissue characterization through quantitative imaging.

Spectral demultiplexing in holographic and fluorescent on-chip microscopy.

Lensfree on-chip imaging and sensing platforms provide compact and cost-effective designs for various telemedicine and lab-on-a-chip applications. In this work, we demonstrate computational solutions for some of the challenges associated with (i) the use of broadband, partially-coherent illumination sources for on-chip holographic imaging, and (ii) multicolor detection for lensfree fluorescent on-chip microscopy. Specifically, we introduce spectral demultiplexing approaches that aim to digitally narrow the spectral content of broadband illumination sources (such as wide-band light emitting diodes or even sunlight) to improve spatial resolution in holographic on-chip microscopy. We also demonstrate the application of such spectral demultiplexing approaches for wide-field imaging of multicolor fluorescent objects on a chip. These computational approaches can be used to replace e.g., thin-film interference filters, gratings or other optical components used for spectral multiplexing/demultiplexing, which can form a desirable solution for cost-effective and compact wide-field microscopy and sensing needs on a chip.

Crowd-sourced BioGames: managing the big data problem for next-generation lab-on-a-chip platforms.

We describe a crowd-sourcing based solution for handling large quantities of data that are created by e.g., emerging digital imaging and sensing devices, including next generation lab-on-a-chip platforms. We show that in cases where the diagnosis is a binary decision (e.g., positive vs. negative, or infected vs. uninfected), it is possible to make accurate diagnosis by crowd-sourcing the raw data (e.g., microscopic images of specimens/cells) using entertaining digital games (i.e., ) that are played on PCs, tablets or mobile phones. We report the results and the analysis of a large-scale public experiment toward diagnosis of malaria infected human red blood cells (RBCs), where binary responses from approximately 1000 untrained individuals from more than 60 different countries are combined together (corresponding to more than 1 million cell diagnoses), resulting in an accuracy level that is comparable to those of expert medical professionals. This platform holds promise toward cost-effective and accurate tele-pathology, improved training of medical personnel, and can also be used to manage the "Big Data" problem that is emerging through next generation digital lab-on-a-chip devices.

Integrated rapid-diagnostic-test reader platform on a cellphone.

We demonstrate a cellphone-based rapid-diagnostic-test (RDT) reader platform that can work with various lateral flow immuno-chromatographic assays and similar tests to sense the presence of a target analyte in a sample. This compact and cost-effective digital RDT reader, weighing only ~65 g, mechanically attaches to the existing camera unit of a cellphone, where various types of RDTs can be inserted to be imaged in reflection or transmission modes under light-emitting diode (LED)-based illumination. Captured raw images of these tests are then digitally processed (within less than 0.2 s per image) through a smart application running on the cellphone for validation of the RDT, as well as for automated reading of its diagnostic result. The same smart application then transmits the resulting data, together with the RDT images and other related information (e.g., demographic data), to a central server, which presents the diagnostic results on a world map through geo-tagging. This dynamic spatio-temporal map of various RDT results can then be viewed and shared using internet browsers or through the same cellphone application. We tested this platform using malaria, tuberculosis (TB) and HIV RDTs by installing it on both Android-based smartphones and an iPhone. Providing real-time spatio-temporal statistics for the prevalence of various infectious diseases, this smart RDT reader platform running on cellphones might assist healthcare professionals and policymakers to track emerging epidemics worldwide and help epidemic preparedness.

Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.

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.

Quantum dot enabled detection of Escherichia coli using a cell-phone.

We report a cell-phone based Escherichia coli (E. coli) detection platform for screening of liquid samples. In this compact and cost-effective design attached to a cell-phone, we utilize anti-E. coli O157:H7 antibody functionalized glass capillaries as solid substrates to perform a quantum dot based sandwich assay for specific detection of E. coli O157:H7 in liquid samples. Using battery-powered inexpensive light-emitting-diodes (LEDs) we excite/pump these labelled E. coli particles captured on the capillary surface, where the emission from the quantum dots is then imaged using the cell-phone camera unit through an additional lens that is inserted between the capillary and the cell-phone. By quantifying the fluorescent light emission from each capillary tube, the concentration of E. coli in the sample is determined. We experimentally confirmed the detection limit of this cell-phone based fluorescent imaging and sensing platform as ∼5 to 10 cfu mL(-1) in buffer solution. We also tested the specificity of this E. coli detection platform by spiking samples with different species (e.g., Salmonella) to confirm that non-specific binding/detection is negligible. We further demonstrated the proof-of-concept of our approach in a complex food matrix, e.g., fat-free milk, where a similar detection limit of ∼5 to 10 cfu mL(-1) was achieved despite challenges associated with the density of proteins that exist in milk. Our results reveal the promising potential of this cell-phone enabled field-portable and cost-effective E. coli detection platform for e.g., screening of water and food samples even in resource limited environments. The presented platform can also be applicable to other pathogens of interest through the use of different antibodies.

Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging.

We report a field-portable lensfree microscope that can image dense and connected specimens with sub-micron resolution over a large field-of-view of ~30 mm(2) (i.e., ~6.4 mm × ~4.6 mm) using pixel super-resolution and iterative phase recovery techniques. Weighing ~122 grams with dimensions of 4 cm × 4 cm × 15 cm, this microscope records lensfree in-line holograms of specimens onto an opto-electronic sensor-array using partially coherent illumination. To reconstruct the phase and amplitude images of dense samples (with >0.3 billion pixels in each image, i.e., >0.6 billion pixels total), we employ a multi-height imaging approach, where by using a mechanical interface the sensor-to-sample distance is dynamically changed by random discrete steps of e.g., ~10 to 80 µm. By digitally propagating back and forth between these multi-height super-resolved holograms (corresponding to typically 2-5 planes), phase and amplitude images of dense samples can be recovered without the need for any spatial masks or filtering. We demonstrate the performance of this field-portable multi-height lensfree microscope by imaging Papanicolaou smears (also known as Pap tests). Our results reveal the promising potential of this multi-height lensfree computational microscopy platform for e.g., pathology needs in resource limited settings.

Field-portable lensfree tomographic microscope.

We present a field-portable lensfree tomographic microscope, which can achieve sectional imaging of a large volume (∼20 mm(3)) on a chip with an axial resolution of <7 µm. In this compact tomographic imaging platform (weighing only ∼110 grams), 24 light-emitting diodes (LEDs) that are each butt-coupled to a fibre-optic waveguide are controlled through a cost-effective micro-processor to sequentially illuminate the sample from different angles to record lensfree holograms of the sample that is placed on the top of a digital sensor array. In order to generate pixel super-resolved (SR) lensfree holograms and hence digitally improve the achievable lateral resolution, multiple sub-pixel shifted holograms are recorded at each illumination angle by electromagnetically actuating the fibre-optic waveguides using compact coils and magnets. These SR projection holograms obtained over an angular range of ±50° are rapidly reconstructed to yield projection images of the sample, which can then be back-projected to compute tomograms of the objects on the sensor-chip. The performance of this compact and light-weight lensfree tomographic microscope is validated by imaging micro-beads of different dimensions as well as a Hymenolepis nana egg, which is an infectious parasitic flatworm. Achieving a decent three-dimensional spatial resolution, this field-portable on-chip optical tomographic microscope might provide a useful toolset for telemedicine and high-throughput imaging applications in resource-poor settings.

Holographic pixel super-resolution in portable lensless on-chip microscopy using a fiber-optic array.

We report a portable lensless on-chip microscope that can achieve <1 µm resolution over a wide field-of-view of ∼ 24 mm(2) without the use of any mechanical scanning. This compact on-chip microscope weighs ∼ 95 g and is based on partially coherent digital in-line holography. Multiple fiber-optic waveguides are butt-coupled to light emitting diodes, which are controlled by a low-cost micro-controller to sequentially illuminate the sample. The resulting lensfree holograms are then captured by a digital sensor-array and are rapidly processed using a pixel super-resolution algorithm to generate much higher resolution holographic images (both phase and amplitude) of the objects. This wide-field and high-resolution on-chip microscope, being compact and light-weight, would be important for global health problems such as diagnosis of infectious diseases in remote locations. Toward this end, we validate the performance of this field-portable microscope by imaging human malaria parasites (Plasmodium falciparum) in thin blood smears. Our results constitute the first-time that a lensfree on-chip microscope has successfully imaged malaria parasites.

Portable and cost-effective pixel super-resolution on-chip microscope for telemedicine applications.

We report a field-portable lensless on-chip microscope with a lateral resolution of <1 µm and a large field-of-view of ~24 mm(2). This microscope is based on digital in-line holography and a pixel super-resolution algorithm to process multiple lensfree holograms and obtain a single high-resolution hologram. In its compact and cost-effective design, we utilize 23 light emitting diodes butt-coupled to 23 multi-mode optical fibers, and a simple optical filter, with no moving parts. Weighing only ~95 grams, we demonstrate the performance of this field-portable microscope by imaging various objects including human malaria parasites in thin blood smears.

Lensfree On-Chip Microscopy and Tomography for Bio-Medical Applications.

Lensfree on-chip holographic microscopy is an emerging technique that offers imaging of biological specimens over a large field-of-view without using any lenses or bulky optical components. Lending itself to a compact, cost-effective and mechanically robust architecture, lensfree on-chip holographic microscopy can offer an alternative toolset addressing some of the emerging needs of microscopic analysis and diagnostics in low-resource settings, especially for telemedicine applications. In this review, we summarize the latest achievements in lensfree optical microscopy based on partially coherent on-chip holography, including portable telemedicine microscopy, cell-phone based microscopy and field-portable optical tomographic microscopy. We also discuss some of the future directions for telemedicine microscopy and its prospects to help combat various global health challenges.