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Your search keyword '"Sadek, Ibrahim"' showing total 14 results
Start Over Author "Sadek, Ibrahim" Journal biomedical optics express
14 results on '"Sadek, Ibrahim"'

3. Deep convolutional neural networks-based scatterer density and resolution estimators in optical coherence tomography: erratum

Author

Seesan, Thitiya, primary, Abd El-Sadek, Ibrahim, additional, Mukherjee, Pradipta, additional, Zhu, Lida, additional, Oikawa, Kensuke, additional, Miyazawa, Arata, additional, Shen, Larina Tzu-Wei, additional, Matsusaka, Satoshi, additional, Buranasiri, Prathan, additional, Makita, Shuichi, additional, and Yasuno, Yoshiaki, additional

Published
2024
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4. A theoretical model for en face optical coherencetomography imaging and its application tovolumetric differential contrast imaging

Author

Tomita, Kiriko, primary, Makita, Shuichi, additional, Fukutake, Naoki, additional, MORISHITA, RION, additional, Abd El-Sadek, Ibrahim, additional, Mukherjee, Pradipta, additional, Lichtenegger, Antonia, additional, Tamaoki, Junya, additional, Bian, Lixuan, additional, Kobayashi, Makoto, additional, Mori, Tomoko, additional, Matsusaka, Satoshi, additional, and Yasuno, Yoshiaki, additional

Published
2023
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6. Label-free metabolic imaging of non-alcoholic-fatty-liver-disease (NAFLD) liver by volumetric dynamic optical coherence tomography

Author

Mukherjee, Pradipta, primary, Fukuda, Shinichi, additional, Lukmanto, Donny, additional, Yamashita, Toshiharu, additional, Okada, Kosuke, additional, Makita, Shuichi, additional, Abd El-Sadek, Ibrahim, additional, Miyazawa, Arata, additional, Zhu, Lida, additional, Morishita, Rion, additional, Lichtenegger, Antonia, additional, Oshika, Tetsuro, additional, and Yasuno, Yoshiaki, additional

Published
2022
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7. Non-destructive characterization of adult zebrafish models using Jones matrix optical coherence tomography

Author

Lichtenegger, Antonia, primary, Mukherjee, Pradipta, additional, Zhu, Lida, additional, Morishita, Rion, additional, Tomita, Kiriko, additional, Oida, Daisuke, additional, Leskovar, Konrad, additional, Abd El-Sadek, Ibrahim, additional, Makita, Shuichi, additional, Kirchberger, Stefanie, additional, Distel, Martin, additional, Baumann, Bernhard, additional, and Yasuno, Yoshiaki, additional

Published
2022
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8. Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography

Author

Seesan, Thitiya, primary, Abd El-Sadek, Ibrahim, additional, Mukherjee, Pradipta, additional, Zhu, Lida, additional, Oikawa, Kensuke, additional, Miyazawa, Arata, additional, Shen, Larina Tzu-Wei, additional, Matsusaka, Satoshi, additional, Buranasiri, Prathan, additional, Makita, Shuichi, additional, and Yasuno, Yoshiaki, additional

Published
2021
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10. Optical coherence tomography-based tissue dynamics imaging for longitudinal and drug response evaluation of tumor spheroids

Author

Abd El-Sadek, Ibrahim, primary, Miyazawa, Arata, additional, Tzu-Wei Shen, Larina, additional, Makita, Shuichi, additional, Fukuda, Shinichi, additional, Yamashita, Toshiharu, additional, Oka, Yuki, additional, Mukherjee, Pradipta, additional, Matsusaka, Satoshi, additional, Oshika, Tetsuro, additional, Kano, Hideaki, additional, and Yasuno, Yoshiaki, additional

Published
2020
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11. Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography

Author

Seesan, Thitiya, Abd El-Sadek, Ibrahim, Mukherjee, Pradipta, Zhu, Lida, Oikawa, Kensuke, Miyazawa, Arata, Shen, Larina Tzu-Wei, Matsusaka, Satoshi, Buranasiri, Prathan, Makita, Shuichi, and Yasuno, Yoshiaki

Abstract

We present deep convolutional neural network (DCNN)-based estimators of the tissue scatterer density (SD), lateral and axial resolutions, signal-to-noise ratio (SNR), and effective number of scatterers (ENS, the number of scatterers within a resolution volume). The estimators analyze the speckle pattern of an optical coherence tomography (OCT) image in estimating these parameters. The DCNN is trained by a large number (1,280,000) of image patches that are fully numerically generated in OCT imaging simulation. Numerical and experimental validations were performed. The numerical validation shows good estimation accuracy as the root mean square errors were 0.23%, 3.65%, 3.58%, 3.79%, and 6.15% for SD, lateral and axial resolutions, SNR, and ENS, respectively. The experimental validation using scattering phantoms (Intralipid emulsion) shows reasonable estimations. Namely, the estimated SDs were proportional to the Intralipid concentrations, and the average estimation errors of lateral and axial resolutions were 1.36% and 0.68%, respectively. The scatterer density estimator was also applied to an in vitro tumor cell spheroid, and a reduction in the scatterer density during cell necrosis was found.

Published
2022

12. Erratum: Deep convolutional neural networks-based scatterer density and resolution estimators in optical coherence tomography: erratum.

Author

Seesan T, Abd El-Sadek I, Mukherjee P, Zhu L, Oikawa K, Miyazawa A, Shen LT, Matsusaka S, Buranasiri P, Makita S, and Yasuno Y

Abstract

[This corrects the article on p. 168 in vol. 13, PMID: 35154862.]., Competing Interests: Seesan, Abd El-Sadek, Mukherjee, Zhu, Oikawa: Yokogawa Electric Corp. (F), Sky technology (F), Nikon (F), Kao Corp. (F), Topcon (F). Miyazawa: Yokogawa Electric Corp. (F), Sky technology (F, E), Nikon (F), Kao Corp. (F), Topcon (F). Shen, Matsusaka: None. Makita, Yasuno: Yokogawa Electric Corp. (F), Sky technology (F), Nikon (F), Kao Corp. (F), Topcon (F), Tomey Corp (P). Buranasiri: None., (© 2024 Optica Publishing Group.)

Published
2024
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13. Theoretical model for en face optical coherence tomography imaging and its application to volumetric differential contrast imaging.

Author

Tomita K, Makita S, Fukutake N, Morishita R, Abd El-Sadek I, Mukherjee P, Lichtenegger A, Tamaoki J, Bian L, Kobayashi M, Mori T, Matsusaka S, and Yasuno Y

Abstract

A new formulation of the lateral imaging process of point-scanning optical coherence tomography (OCT) and a new differential contrast method designed by using this formulation are presented. The formulation is based on a mathematical sample model called the dispersed scatterer model (DSM), in which the sample is represented as a material with a spatially slowly varying refractive index and randomly distributed scatterers embedded in the material. It is shown that the formulation represents a meaningful OCT image and speckle as two independent mathematical quantities. The new differential contrast method is based on complex signal processing of OCT images, and the physical and numerical imaging processes of this method are jointly formulated using the same theoretical strategy as in the case of OCT. The formula shows that the method provides a spatially differential image of the sample structure. This differential imaging method is validated by measuring in vivo and in vitro samples., Competing Interests: Tomita, Makita, Morishita, Abd El-Sadek, Mukherjee, Lichtenegger, Yasuno: Nikon (F), Sky Technology (F), Yokogawa Electric Corp. (F), Kao Corp. (F), Topcon (F). Fukutake: Nikon (E). Tamaoki, Bian, Kobayashi: None. Mori, Matsusaka: None., (Published by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.)

Published
2023
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14. Label-free intratissue activity imaging of alveolar organoids with dynamic optical coherence tomography.

Author

Morishita R, Suzuki T, Mukherjee P, Abd El-Sadek I, Lim Y, Lichtenegger A, Makita S, Tomita K, Yamamoto Y, Nagamoto T, and Yasuno Y

Abstract

An organoid is a three-dimensional (3D) in vitro cell culture emulating human organs. We applied 3D dynamic optical coherence tomography (DOCT) to visualize the intratissue and intracellular activities of human induced pluripotent stem cells (hiPSCs)-derived alveolar organoids in normal and fibrosis models. 3D DOCT data were acquired with an 840-nm spectral domain optical coherence tomography with axial and lateral resolutions of 3.8 µm (in tissue) and 4.9 µm, respectively. The DOCT images were obtained by the logarithmic-intensity-variance (LIV) algorithm, which is sensitive to the signal fluctuation magnitude. The LIV images revealed cystic structures surrounded by high-LIV borders and mesh-like structures with low LIV. The former may be alveoli with a highly dynamics epithelium, while the latter may be fibroblasts. The LIV images also demonstrated the abnormal repair of the alveolar epithelium., Competing Interests: Morishita, Mukherjee, Abd El-Sadek, Lim, Makita, Tomita, Yasuno: Yokogawa Electric Corp. (F), Sky Technology (F), Nikon (F), Kao Corp. (F), Topcon (F), Tomey Corp (F). Suzuki: Chugai Pharmaceutical Co, Ltd (R), Nippon Boehringer Ingelheim (R), Sysmex Corporation (R). Lichtenegger: None. Yamamoto: HiLung Inc. (I, P). Nagamoto: HiLung Inc. (I)., (Published by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.)

Published
2023
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