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Speeding Up the Line-Scan Raman Imaging of Living Cells by Deep Convolutional Neural Network.
He, Hao; Xu, Mengxi; Zong, Cheng; Zheng, Peng; Luo, Lilan; Wang, Lei; Ren, Bin.
Afiliación
  • He H; School of Aerospace Engineering , Xiamen University , Xiamen 361005 , P. R. China.
  • Xu M; The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering , Xiamen University , Xiamen 361005 , P. R. C
  • Zong C; The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering , Xiamen University , Xiamen 361005 , P. R. C
  • Zheng P; School of Aerospace Engineering , Xiamen University , Xiamen 361005 , P. R. China.
  • Luo L; School of Aerospace Engineering , Xiamen University , Xiamen 361005 , P. R. China.
  • Wang L; School of Aerospace Engineering , Xiamen University , Xiamen 361005 , P. R. China.
  • Ren B; The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering , Xiamen University , Xiamen 361005 , P. R. C
Anal Chem ; 91(11): 7070-7077, 2019 06 04.
Article en En | MEDLINE | ID: mdl-31063356
Raman imaging is a promising technique that allows the spatial distribution of different components in the sample to be obtained using the molecular fingerprint information on individual species. However, the imaging speed is the bottleneck for the current Raman imaging methods to monitor the dynamic process of living cells. In this paper, we developed an artificial intelligence assisted fast Raman imaging method over the already fast line scan Raman imaging method. The reduced imaging time is realized by widening the slit and laser beam, and scanning the sample with a large scan step. The imaging quality is improved by a data-driven approach to train a deep convolutional neural network, which statistically learns to transform low-resolution images acquired at a high speed into high-resolution ones that previously were only possible with a low imaging speed. Accompanied with the improvement of the image resolution, the deteriorated spectral resolution as a consequence of a wide slit is also restored, thereby the fidelity of the spectral information is retained. The imaging time can be reduced to within 1 min, which is about five times faster than the state-of-the-art line scan Raman imaging techniques without sacrificing spectral and spatial resolution. We then demonstrated the reliability of the current method using fixed cells. We finally used the method to monitor the dynamic evolution process of living cells. Such an imaging speed opens a door to the label-free observation of cellular events with conventional Raman microscopy.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectrometría Raman / Procesamiento de Imagen Asistido por Computador / Redes Neurales de la Computación Límite: Humans Idioma: En Revista: Anal Chem Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectrometría Raman / Procesamiento de Imagen Asistido por Computador / Redes Neurales de la Computación Límite: Humans Idioma: En Revista: Anal Chem Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos