Abstract
The revolution in low-cost consumer photography and computation provides fertile opportunity for a disruptive reduction in the cost of biomedical imaging. Conventional approaches to low-cost microscopy are fundamentally restricted, however, to modest field of view (FOV) and/or resolution. We report a low-cost microscopy technique, implemented with a Raspberry Pi single-board computer and color camera combined with Fourier ptychography (FP), to computationally construct 25-megapixel images with sub-micron resolution. New image-construction techniques were developed to enable the use of the low-cost Bayer color sensor, to compensate for the highly aberrated re-used camera lens and to compensate for misalignments associated with the 3D-printed microscope structure. This high ratio of performance to cost is of particular interest to high-throughput microscopy applications, ranging from drug discovery and digital pathology to health screening in low-income countries. 3D models and assembly instructions of our microscope are made available for open source use.
Highlights
Low-cost, high-performance portable microscopes are essential tools for disease diagnosis in remote and resource-limited communities[1]
The empty pixels are estimated instead from demosaicing enabling the use of a conventional Fourier-ptychographic microscope (FPM) recovery; we refer to this approach as demosaiced reconstruction (DR)
We report below a comparison of image-recovery accuracy using sampled reconstruction (SSR) and DR recovery applied to simulated data
Summary
Low-cost, high-performance portable microscopes are essential tools for disease diagnosis in remote and resource-limited communities[1]. The Raspberry Pi single-board computer used for controlling the camera and www.nature.com/scientificreports illumination LEDs performs autonomous data acquisition, providing portability and compactness, such as is required for use inside incubation systems.
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