Multi-contrast imaging and digital refocusing on a mobile microscope with a domed LED array

We demonstrate the design and application of an add-on device for improving the diagnostic and research capabilities of CellScope--a low-cost, smartphone-based point-of-care microscope. We replace the single LED illumination of the original CellScope with a programmable domed LED array. By leveraging recent advances in computational illumination, this new device enables simultaneous multi-contrast imaging with brightfield, darkfield, and phase imaging modes. Further, we scan through illumination angles to capture lightfield datasets, which can be used to recover 3D intensity and phase images without any hardware changes. This digital refocusing procedure can be used for either 3D imaging or software-only focus correction, reducing the need for precise mechanical focusing during field experiments. All acquisition and processing is performed on the mobile phone and controlled through a smartphone application, making the computational microscope compact and portable. Using multiple samples and different objective magnifications, we demonstrate that the performance of our device is comparable to that of a commercial microscope. This unique device platform extends the field imaging capabilities of CellScope, opening up new clinical and research possibilities

Confidence Prediction from EEG Recordings in a Multisensory Environment

This paper investigates the possibility of decoding decision confidence from electroencephalographic (EEG) brain activity of human subjects during a multisensory decision-making task. In recent research we have shown that decision confidence correlates could be extracted from EEG recordings during visual or auditory tasks. Here we extend these initial findings by (a) predicting the confidence in the decision from EEG recordings alone, and (b) investigating the impact of multisensory cues on decision-making behavioral data. Our results obtained from 12 participants recorded at two different sites show that the decision confidence could be predicted from EEG recordings on a single-trial basis with a mean absolute error of 0.226. Moreover, the presence of a multisensory cue did not improve the performance of the participants, but rather distracted them from the main task. Overall, these results may inform the development of cognitive systems that could monitor and alert users when they are not confident about their decisions

Multi-Contrast Imaging and Digital Refocusing on a Mobile Microscope with a Domed LED Array.

We demonstrate the design and application of an add-on device for improving the diagnostic and research capabilities of CellScope--a low-cost, smartphone-based point-of-care microscope. We replace the single LED illumination of the original CellScope with a programmable domed LED array. By leveraging recent advances in computational illumination, this new device enables simultaneous multi-contrast imaging with brightfield, darkfield, and phase imaging modes. Further, we scan through illumination angles to capture lightfield datasets, which can be used to recover 3D intensity and phase images without any hardware changes. This digital refocusing procedure can be used for either 3D imaging or software-only focus correction, reducing the need for precise mechanical focusing during field experiments. All acquisition and processing is performed on the mobile phone and controlled through a smartphone application, making the computational microscope compact and portable. Using multiple samples and different objective magnifications, we demonstrate that the performance of our device is comparable to that of a commercial microscope. This unique device platform extends the field imaging capabilities of CellScope, opening up new clinical and research possibilities

Computational CellScope LED Dome Design Files

<p>NOTE - Up-to-date versions of this work may be found at:</p> <p>https://github.com/Waller-Lab/CompCellScopeHardware</p> <p>and</p> <p>https://github.com/Waller-Lab/CompCellScopeAndroid</p> <p>-----</p> <p>This file contains all design files necessary to create a domed, programmable, 508 LED Illuminator add-on for the CellScope Device. Included are the CAD design files, PCB design files, Arduino sketches, and Android application for controlling the LED array using a smartphone. The design should also be useful for a variety of other applications invloving a domed LED array.</p

Image Results Compared to a Standard Microscope.

<p>Computational CellScope acquires brightfield and darkfield images of similar quality to a standard upright microscope (Nikon TE300) without the use of hardware inserts. Additionally, it enables phase imaging using Differential Phase Contrast (DPC), which contains similar information to standard phase contrast imaging, and can be inverted to obtain quantitative phase of the sample (bottom row). Differences in color shades are caused by the relative differences in hue of the halogen lamp and the white LEDs. Note the additional dark features in DIC results, as compared to DPC, illustrating mixing of phase and absorption information in DIC. In the rightmost column, we show images for an unstained transparent sample, illustrating the utility of phase imaging methods for label-free imaging.</p

Computational CellScope.

<p><b>A.</b> Device observing a sample using a Nexus 4 smartphone. <b>B.</b> Optical schematic of the CellScope device with our custom-made domed LED illuminator. <b>C.</b> CAD assembly of the dome. <b>D.</b> Assembled dome and control circuitry.</p

Android Application Workflow.

<p><b>A.</b> Schematic of streaming multi-contrast LED patterns. Here we vary the LED pattern in time and acquire and process images on the smartphone, producing a streaming multi-contrast display of a sample without any further post-processing. The user can touch any image to zoom in and stream an individual image. Total cycle time is 2.3 seconds. <b>B.</b> Overview of workflow for digital refocusing mode. Table shows example processing and acquisition times for a typical dataset reconstruction. Axial Resolution is determined by the range of illumination angles sampled (defined by the objective NA). The number of z-steps were chosen such that refocus blur does not exceed 20 pixels. Processing and acquisition time can be reduced by selecting fewer refocus planes or by sparsely sampling LEDs, trading axial resolution for faster acquisition time.</p