Deep learning acceleration of multiscale superresolution localization photoacoustic imaging

Jongbeom Kim; Gyuwon Kim; Lei Li; Pengfei Zhang; Jin Young Kim; Yeonggeun Kim; Hyung Ham Kim; Lihong V. Wang; Seungchul Lee; Chulhong Kim

doi:10.1038/s41377-022-00820-w

Light: Science & Applications (May 2022)

Deep learning acceleration of multiscale superresolution localization photoacoustic imaging

Jongbeom Kim,
Gyuwon Kim,
Lei Li,
Pengfei Zhang,
Jin Young Kim,
Yeonggeun Kim,
Hyung Ham Kim,
Lihong V. Wang,
Seungchul Lee,
Chulhong Kim

Affiliations

Jongbeom Kim: Departments of Electrical Engineering, Mechanical Engineering, Convergence IT Engineering, and Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH)
Gyuwon Kim: Departments of Electrical Engineering, Mechanical Engineering, Convergence IT Engineering, and Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH)
Lei Li: Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology
Pengfei Zhang: School of Precision Instruments and Optoelectronics Engineering, Tianjin University
Jin Young Kim: Departments of Electrical Engineering, Mechanical Engineering, Convergence IT Engineering, and Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH)
Yeonggeun Kim: Departments of Electrical Engineering, Mechanical Engineering, Convergence IT Engineering, and Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH)
Hyung Ham Kim: Departments of Electrical Engineering, Mechanical Engineering, Convergence IT Engineering, and Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH)
Lihong V. Wang: Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology
Seungchul Lee: Departments of Electrical Engineering, Mechanical Engineering, Convergence IT Engineering, and Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH)
Chulhong Kim: Departments of Electrical Engineering, Mechanical Engineering, Convergence IT Engineering, and Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH)

DOI: https://doi.org/10.1038/s41377-022-00820-w
Journal volume & issue: Vol. 11, no. 1
pp. 1 – 12

Abstract

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Computational strategy based on deep neural networks enables to reconstruct high-density superresolution localization photoacoustic images from far fewer raw image frames.

Published in Light: Science & Applications

ISSN: 2047-7538 (Online)
Publisher: Nature Publishing Group
Country of publisher: United Kingdom
LCC subjects: Technology: Engineering (General). Civil engineering (General): Applied optics. Photonics; Science: Physics: Optics. Light
Website: https://www.nature.com/lsa/

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