Sam Mavandadi

Optofluidic Fluorescent Imaging Cytometry on a Cell Phone

Fluorescent microscopy and flow cytometry are widely used tools in biomedical sciences. Cost-effective translation of these technologies to remote and resource-limited environments could create new opportunities especially for telemedicine applications. Toward this direction, here we demonstrate the integration of imaging cytometry and fluorescent microscopy on a cell phone using a compact, lightweight, and cost-effective optofluidic attachment. In this cell-phone-based optofluidic imaging cytometry platform, fluorescently labeled particles or cells of interest are continuously delivered to our imaging volume through a disposable microfluidic channel that is positioned above the existing camera unit of the cell phone. The same microfluidic device also acts as a multilayered optofluidic waveguide and efficiently guides our excitation light, which is butt-coupled from the side facets of our microfluidic channel using inexpensive light-emitting diodes. Since the excitation of the sample volume occurs through guided waves that propagate perpendicular to the detection path, our cell-phone camera can record fluorescent movies of the specimens as they are flowing through the microchannel. The digital frames of these fluorescent movies are then rapidly processed to quantify the count and the density of the labeled particles/cells within the target solution of interest. We tested the performance of our cell-phone-based imaging cytometer by measuring the density of white blood cells in human blood samples, which provided a decent match to a commercially available hematology analyzer. We further characterized the imaging quality of the same platform to demonstrate a spatial resolution of ~2 μm. This cell-phone-enabled optofluidic imaging flow cytometer could especially be useful for rapid and sensitive imaging of bodily fluids for conducting various cell counts (e.g., toward monitoring of HIV+ patients) or rare cell analysis as well as for screening of water quality in remote and resource-poor settings.

Lens-free optical tomographic microscope with a large imaging volume on a chip

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 × < 1 μm × < 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., ≤ 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.

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.

A mathematical framework for combining decisions of multiple experts toward accurate and remote diagnosis of malaria using tele-microscopy.

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.

Fluorescent flow-cytometry on a cell-phone

We demonstrate fluorescent imaging flow-cytometry that is integrated on a cell-phone. The cellphone based flow-cytometer was used to measure the density of white-blood-cells in blood samples, providing a decent match to the hematology analyzer.

Lensless Tomographic Microscopy on a Chip

A lensless optical tomography platform is demonstrated for use in high throughput 3D imaging applications. Through the use of pixel super-resolution techniques in partially-coherent digital in-line holography and tomographic reconstruction, this computational microscope achieves <1µm × <1µm × <3µm spatial resolution along the x, y and z directions, respectively, over a large imaging volume of ~15mm3.