Cargando…

Large depth-of-field ultra-compact microscope by progressive optimization and deep learning

The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report an integrated microscope that achieves optical performance beyond a commercial microscope with a 5×, NA 0.1 objective but only at 0.15 cm(3) and 0.5 g, whose size is five order...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Yuanlong, Song, Xiaofei, Xie, Jiachen, Hu, Jing, Chen, Jiawei, Li, Xiang, Zhang, Haiyu, Zhou, Qiqun, Yuan, Lekang, Kong, Chui, Shen, Yibing, Wu, Jiamin, Fang, Lu, Dai, Qionghai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336131/
https://www.ncbi.nlm.nih.gov/pubmed/37433856
http://dx.doi.org/10.1038/s41467-023-39860-0
_version_ 1785071144165965824
author Zhang, Yuanlong
Song, Xiaofei
Xie, Jiachen
Hu, Jing
Chen, Jiawei
Li, Xiang
Zhang, Haiyu
Zhou, Qiqun
Yuan, Lekang
Kong, Chui
Shen, Yibing
Wu, Jiamin
Fang, Lu
Dai, Qionghai
author_facet Zhang, Yuanlong
Song, Xiaofei
Xie, Jiachen
Hu, Jing
Chen, Jiawei
Li, Xiang
Zhang, Haiyu
Zhou, Qiqun
Yuan, Lekang
Kong, Chui
Shen, Yibing
Wu, Jiamin
Fang, Lu
Dai, Qionghai
author_sort Zhang, Yuanlong
collection PubMed
description The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report an integrated microscope that achieves optical performance beyond a commercial microscope with a 5×, NA 0.1 objective but only at 0.15 cm(3) and 0.5 g, whose size is five orders of magnitude smaller than that of a conventional microscope. To achieve this, a progressive optimization pipeline is proposed which systematically optimizes both aspherical lenses and diffractive optical elements with over 30 times memory reduction compared to the end-to-end optimization. By designing a simulation-supervision deep neural network for spatially varying deconvolution during optical design, we accomplish over 10 times improvement in the depth-of-field compared to traditional microscopes with great generalization in a wide variety of samples. To show the unique advantages, the integrated microscope is equipped in a cell phone without any accessories for the application of portable diagnostics. We believe our method provides a new framework for the design of miniaturized high-performance imaging systems by integrating aspherical optics, computational optics, and deep learning.
format Online
Article
Text
id pubmed-10336131
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-103361312023-07-13 Large depth-of-field ultra-compact microscope by progressive optimization and deep learning Zhang, Yuanlong Song, Xiaofei Xie, Jiachen Hu, Jing Chen, Jiawei Li, Xiang Zhang, Haiyu Zhou, Qiqun Yuan, Lekang Kong, Chui Shen, Yibing Wu, Jiamin Fang, Lu Dai, Qionghai Nat Commun Article The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report an integrated microscope that achieves optical performance beyond a commercial microscope with a 5×, NA 0.1 objective but only at 0.15 cm(3) and 0.5 g, whose size is five orders of magnitude smaller than that of a conventional microscope. To achieve this, a progressive optimization pipeline is proposed which systematically optimizes both aspherical lenses and diffractive optical elements with over 30 times memory reduction compared to the end-to-end optimization. By designing a simulation-supervision deep neural network for spatially varying deconvolution during optical design, we accomplish over 10 times improvement in the depth-of-field compared to traditional microscopes with great generalization in a wide variety of samples. To show the unique advantages, the integrated microscope is equipped in a cell phone without any accessories for the application of portable diagnostics. We believe our method provides a new framework for the design of miniaturized high-performance imaging systems by integrating aspherical optics, computational optics, and deep learning. Nature Publishing Group UK 2023-07-11 /pmc/articles/PMC10336131/ /pubmed/37433856 http://dx.doi.org/10.1038/s41467-023-39860-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zhang, Yuanlong
Song, Xiaofei
Xie, Jiachen
Hu, Jing
Chen, Jiawei
Li, Xiang
Zhang, Haiyu
Zhou, Qiqun
Yuan, Lekang
Kong, Chui
Shen, Yibing
Wu, Jiamin
Fang, Lu
Dai, Qionghai
Large depth-of-field ultra-compact microscope by progressive optimization and deep learning
title Large depth-of-field ultra-compact microscope by progressive optimization and deep learning
title_full Large depth-of-field ultra-compact microscope by progressive optimization and deep learning
title_fullStr Large depth-of-field ultra-compact microscope by progressive optimization and deep learning
title_full_unstemmed Large depth-of-field ultra-compact microscope by progressive optimization and deep learning
title_short Large depth-of-field ultra-compact microscope by progressive optimization and deep learning
title_sort large depth-of-field ultra-compact microscope by progressive optimization and deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336131/
https://www.ncbi.nlm.nih.gov/pubmed/37433856
http://dx.doi.org/10.1038/s41467-023-39860-0
work_keys_str_mv AT zhangyuanlong largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT songxiaofei largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT xiejiachen largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT hujing largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT chenjiawei largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT lixiang largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT zhanghaiyu largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT zhouqiqun largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT yuanlekang largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT kongchui largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT shenyibing largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT wujiamin largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT fanglu largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning
AT daiqionghai largedepthoffieldultracompactmicroscopebyprogressiveoptimizationanddeeplearning