{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Aidukas T"],"funding":["RCUK | Engineering and Physical Sciences Research Council","Engineering and Physical Sciences Research Council"],"pagination":["7457"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6520337"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_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."],"journal":["Scientific reports"],"pubmed_title":["Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware."],"pmcid":["PMC6520337"],"funding_grant_id":["1807419","EP/L016753/1"],"pubmed_authors":["Konda PC","Eckert R","Harvey AR","Aidukas T","Waller L"],"additional_accession":[]},"is_claimable":false,"name":"Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware.","description":"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.","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 May","modification":"2026-05-06T21:53:20.27Z","creation":"2019-06-06T23:15:53Z"},"accession":"S-EPMC6520337","cross_references":{"pubmed":["31092867"],"doi":["10.1038/s41598-019-43845-9"]}}