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Ihara Shiro Last modified date：2023.07.02

Assistant Professor / Department of Integrated Materials
Institute for Materials Chemistry and Engineering


Undergraduate School
School of Engineering


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Homepage
https://kyushu-u.pure.elsevier.com/en/persons/shiro-ihara
　Reseacher Profiling Tool　Kyushu University Pure
Academic Degree
PhD(Engineering)
Field of Specialization
Materials Engineering, Crystal Lattice Defects, Machine Learning
Total Priod of education and research career in the foreign country
00years00months
Research
Research Interests
  • Improving microscopic images and application for in-situ experiments by machine-learning
    keyword : Machine learning, Electron Microscope, Dislocation
    2021.04.
Academic Activities
Papers
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1. Shiro Ihara, Mizumo Yoshinaga, Hiroya Miyazaki, Kota Wada, Satoshi Hata, Hikaru Saito and Mitsuhiro Murayama, In situ electron tomography for the thermally activated solid reaction of anaerobic nanoparticles, Nanoscale, https://doi.org/10.1039/D3NR00992K, 15, 10133-10140, 2023.06.
2. S. Ihara,H. Saito,M. Yoshinaga,L. Avala,M. Murayama , Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy, Sci. Rep., https://doi.org/10.1038/s41598-022-17360-3, 12, 13462, 2022.08, Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM’s high affinity with various analytical options into account. As is well known, STEM’s image acquisition time needs to be further shortened to capture a targeted phenomenon in real-time as STEM’s current temporal resolution is far below the conventional TEM’s. However, rapid image acquisition in the millisecond per frame or faster generally causes image distortion, poor electron signals, and unidirectional blurring, which are obstacles for realizing video-rate STEM observation. Here we show an image correction framework integrating deep learning (DL)-based denoising and image distortion correction schemes optimized for STEM rapid image acquisition. By comparing a series of distortion corrected rapid scan images with corresponding regular scan speed images, the trained DL network is shown to remove not only the statistical noise but also the unidirectional blurring. This result demonstrates that rapid as well as high-quality image acquisition by STEM without hardware modification can be established by the DL. The DL-based noise filter could be applied to in-situ observation, such as dislocation activities under external stimuli, with high spatio-temporal resolution..
3. Y. Zhao, S. Koike, R. Nakama, S. Ihara, M. Mitsuhara, M. Murayama, S. Hata and H. Saito, Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering, Sci. Rep., 11, 20720, 2021.10, Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively
thick specimen than the conventional transmission electron microscopy, whose resolution is
limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been
employed frequently for computed electron tomography based three-dimensional (3D) structural
characterization and combined with analytical methods such as annular dark field imaging or
spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially
when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate
a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only
within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The
developed method offers a new platform for various in situ or operando 3D microanalyses in which
dealing with relatively thick specimens or covering media like liquid cells are required..
Membership in Academic Society
  • The Physical Society of Japan
  • The Japanese Society of Microscopy
  • The Japan Society for Technology of Plasticity
  • The Society of Materials Science, Japan
  • The Japan Society of Mechanical Engineers


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