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3DM: deep decomposition and deconvolution microscopy for rapid neural activity imaging
Author(s) -
Eun-Seo Cho,
Seungjae Han,
Kang-Han Lee,
Cheol-Hee Kim,
Young-Gyu Yoon
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.439619
Subject(s) - deconvolution , microscopy , inverse problem , artificial neural network , decomposition , optics , artificial intelligence , computer science , algorithm , physics , chemistry , mathematics , mathematical analysis , organic chemistry
We report the development of deep decomposition and deconvolution microscopy (3DM), a computational microscopy method for the volumetric imaging of neural activity. 3DM overcomes the major challenge of deconvolution microscopy, the ill-posed inverse problem. We take advantage of the temporal sparsity of neural activity to reformulate and solve the inverse problem using two neural networks which perform sparse decomposition and deconvolution. We demonstrate the capability of 3DM via in vivo imaging of the neural activity of a whole larval zebrafish brain with a field of view of 1040 µm × 400 µm × 235 µm and with estimated lateral and axial resolutions of 1.7 µm and 5.4 µm, respectively, at imaging rates of up to 4.2 volumes per second.

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