Hardware

The Computational CellScope hardware involves a custom-built domed LED illuminator attached to an inverted variant of the CellScope smartphone-based microscope platform (see Fig 1). The CellScope used here is a finite-conjugate transmission microscope coupled to an Android-based Nexus 5 smartphone (LG Electronics/Google) as described in in Skandarajah, et al. [14]. Our domed illuminator hardware is compatible with all smartphones and tablets that are used with the existing CellScope, including the iPhone 4S, 5, 5S, and 6 (Apple, Inc.), as well as several Android devices. Phones are mounted via modular 3D printed mounts adapted to each specific smartphone model. Hardware changes were entirely on the illumination side, where we have replaced the original single LED light with our domed illuminator consisting of 508 individually addressable broad spectrum (white) LEDs. Our domed LED arrangement was inspired by the opto-mechanical geometry of the AWARE gigapixel camera [31]. LEDs are uniformly distributed in an (approximately) hexagonal packing pattern across a 77 degree cone of angles corresponding to an illumination NA of 0.62. Thus, darkfield imaging is feasible for objectives with NA smaller than 0.62 (as illustrated in Fig 2F), and both phase and digital refocusing are possible for all objective NAs. The dome assembly was is secured to a custom stage that attached to the top of the CellScope objective; the stage and circuit board holders were 3D printed using low-cost ABS plastic. In general, the design is modular and features simple electronics, including the use of the inexpensive and widely used Arduino micro-controller platform. Phone mounts can be swapped out for upgrading to new models and objectives can be replaced for varying the magnification of the system. While our addition involves custom LED drive circuitry and a 3D printed structure, complexity was kept low to preserve the low-cost nature of CellScope. Part counts, cost and especially size may be further reduced in design-for-manufacture. The size of the illuminator could be reduced to essentially the dimensions of the dome itself, and cost could be comparable to the price of a modern smartphone, matching and improving upon the functionality of a full-size microscope at a fraction of the cost.

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larger image TIFF original image Download: Fig 1. Computational CellScope. A. Device observing a sample using a Nexus 4 smartphone. B. Optical schematic of the CellScope device with our custom-made domed LED illuminator. C. CAD assembly of the dome. D. Assembled dome and control circuitry. https://doi.org/10.1371/journal.pone.0124938.g001

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larger image TIFF original image Download: Fig 2. Domed LED Illuminator. A. Visual comparison of a planar LED array with a domed array. Since the intensity of a spherical wave drops as a function of the inverse square of radius, the illumination at the sample depends on the distance between the LEDs and the sample. In the planar case (left), LED distance r increases as a function of illumination angle, causing weaker illumination at higher angles. A domed LED array (right) eliminates this variation (r is constant). B. Normalized mean pixel intensities measured at the sensor for the planar and domed arrays. Intensity decreases as a function of angle in both cases, but much more strongly in the case of the planar geometry. Values were normalized to the central LED’s brightness in both cases. C. Illumination pattern used to acquire dark field images with a 0.25 NA objective. D. Illumination pattern used to synthesize differential phase contrast images with a 0.25 NA objective. E. Illustration of the arbitrary illumination patterning capabilities of the device. F. Plot illustrating the relative objective NA for several common magnifications, as compared to our dome’s LED placement (small black circles). G. Normalized measured intensity falloff as a function of angle relative to the optical axis for the domed and planar LED arrays. Falloff is proportional to cos θ for the domed geometry and ∼ cos4 θ for the planar geometry. Black lines are cos θ and cos4 θ fits for the domed and planar geometries, respectively. The domed geometry exhibits significant improvements in intensity at large angles of illumination. https://doi.org/10.1371/journal.pone.0124938.g002

The domed LED arrangement provides significantly better light efficiency than the planar LED arrays used in previous work, enabling shorter acquisition times and more efficient power use. These advantages could be crucial for mobile microscopy applications where power is a scarce resource, and shorter exposure times reduce motion blur artifacts due to unstable experimental conditions. The power benefits are a result of two phenomena, shown in Fig 2A. The first is that off-axis LEDs in a planar array will have a larger LED-to-sample distance and thus decreased intensity at the sample. For example, if we assume that each LED is a point emitter, the intensity falloff due to increased distance can be expressed as I(θ) = I 0 cos2 θ, where I 0 is the intensity at the sample from the on-axis LED and is illumination angle. The second improvement in light efficiency comes from the fact that LEDs have significant angular variation in intensity (typically emitting more light in the forward direction). In a planar array, the LEDs at higher angles provide less effective illumination, a problem corrected by the dome geometry, where all LEDs are radially oriented. In both the domed and planar geometries we note that intensity further decreases with a final factor of cos θ due to the smaller profile of objective window when viewed off-axis; combining these factors and assuming a Lambertian (∼ cos θ) angular dependence for physical (non-point-source) LEDs results in an expected intensity falloff of ∼ cos4 θ for the planar geometry but only ∼ cos θ for the domed geometry, a vast improvement at high incidence angles. Thus, the difference between geometries is proportional to cos3θ, or a factor of > 50% at 40° and 99% at 77° incidence, having a substantial impact on required exposure times. Such behavior matches well with our experimental measurements (Fig 2B–2G), where the measured intensity is shown for both geometries out to 40 incidence. Variations in intensity between LEDs may also come from electrical variations such as batch differences in controller chips and resistor tolerances.

Multi-Contrast Imaging. To achieve brightfield, darkfield and phase contrast simultaneously, we time-multiplex images taken with different LED patterns and post-process them on the smartphone to synthesize pseudo-real-time multi-contrast imaging, as in [21]. Brightfield images correspond to illumination by LEDs that lie within the cone of angles described by the objective numerical aperture (NA). Darkfield images are obtained by illuminating the sample from angles beyond the angular acceptance of the objective (Fig 2C) [19]. Since different objectives have different NA, one must specify in the software which objective is being used, with larger NA corresponding to a larger brightfield region of LEDs. Our dome is designed to enable darkfield contrast for any objective of NA < 0.62, roughly corresponding to a typical 40× objective. Phase contrast can be achieved in a single-shot image by any asymmetric illumination pattern [32, 33]. Here, we choose to employ a differential phase contrast (DPC) scheme [20, 23, 24, 34], which requires two images having complementary illumination patterns, because it gives good phase contrast at all spatial frequencies and can be quantitatively interpreted. The method involves sequentially illuminating the sample with the two opposite halves of the brightfield circle while capturing an intensity image for each. For example, one may first take an image, I R , with only the right half of the LEDs on and then a second image, I L , with only the left half of LEDs on (see Fig 2D). The two images are processed as follows to obtain brightfield and phase contrast: (1) where I BF is the brightfield image and I DPC is the phase contrast image. Since the LEDs are mutually incoherent, adding the two images gives an equivalent brightfield image and subtracting them produces phase contrast, due to asymmetric clipping in Fourier space. The intensity of the DPC image can be shown to be approximately proportional to the first derivative of phase along the direction of illumination asymmetry [34], and different axes of rotation can be programmed by changing the LED array pattern accordingly. Typically, we capture an additional two images in order to compute both the Left-Right and Top-Bottom phase derivative results representing both orthogonal directions. DPC images are qualitatively similar to Differential Interference Contrast (DIC); however, the latter is not a quantitative method. To obtain quantitative phase from DPC images, we solve the inverse problem [23, 29] using a simple deconvolution in Fourier space, as shown in Fig 3. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 3. Image Results Compared to a Standard Microscope. 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. https://doi.org/10.1371/journal.pone.0124938.g003 Thus, by acquiring two (or four) half-brightfield images and a single darkfield image for each time point, we can synthesize brightfield, darkfield, and phase contrast modes in near real-time. Users have the option of saving and post-processing time-multiplexed frames or viewing a live multi-contrast display of the sample, though display speed is significantly faster in the latter case. We developed an application to stream these four contrast modes size-by-side while updating each frame sequentially as the illumination pattern cycles through the different patterns (Fig 4A). The user may touch any of the four images for a live full-screen display of that contrast mode only, and the illumination pattern cycle will update to reflect this. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 4. Android Application Workflow. A. 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. 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. https://doi.org/10.1371/journal.pone.0124938.g004 Some image results for each of the contrast modes are shown in Fig 3, using different objective magnifications and samples. For comparison, we show the same samples imaged in a commercial inverted microscope with traditional hardware. Darkfield was obtained by using a Ph3 condenser aperture in combination with objectives having NA smaller than the sine of the half-angle of the Ph3 annulus inner diameter. Since DPC is not currently commercially available, we instead compare our DPC phase contrast images to (similar-appearing) DIC. Both provide images whose contrast is related to the first derivative of phase along a single direction; however, DIC mixes absorption and birefringence information with phase, so that dark features in the image may result from either absorption of the sample or phase contrast interferences. In the DPC images, on the other hand, the image is related purely to the sample phase distribution (see Fig 3), which can be inverted to reveal quantitative phase, as shown in the bottom row. Provided in a portable package, these multi-contrast video and streaming methods have the potential to allow clinicians to view a sample with three separate contrast methods at once, enhancing the information available for diagnosis and disease discrimination.