No abstract

We report a portable lensless on-chip microscope that can achieve <1 μm resolution over a wide field-of-view of ~24 mm2 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.

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.

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.

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.