Tutorial on the Use of Deep Learning in Diffuse Optical Tomography
Diffuse optical tomography using deep learning is an emerging technology that has found impressive medical diagnostic applications. However, creating an optical imaging system that uses visible and near-infrared (NIR) light is not straightforward due to photon absorption and multi-scattering by tiss...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/3/305 |
| _version_ | 1797488396714639360 |
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| author | Ganesh M. Balasubramaniam Ben Wiesel Netanel Biton Rajnish Kumar Judy Kupferman Shlomi Arnon |
| author_facet | Ganesh M. Balasubramaniam Ben Wiesel Netanel Biton Rajnish Kumar Judy Kupferman Shlomi Arnon |
| author_sort | Ganesh M. Balasubramaniam |
| collection | DOAJ |
| description | Diffuse optical tomography using deep learning is an emerging technology that has found impressive medical diagnostic applications. However, creating an optical imaging system that uses visible and near-infrared (NIR) light is not straightforward due to photon absorption and multi-scattering by tissues. The high distortion levels caused due to these effects make the image reconstruction incredibly challenging. To overcome these challenges, various techniques have been proposed in the past, with varying success. One of the most successful techniques is the application of deep learning algorithms in diffuse optical tomography. This article discusses the current state-of-the-art diffuse optical tomography systems and comprehensively reviews the deep learning algorithms used in image reconstruction. This article attempts to provide researchers with the necessary background and tools to implement deep learning methods to solve diffuse optical tomography. |
| first_indexed | 2024-03-10T00:02:31Z |
| format | Article |
| id | doaj.art-a030bcd96d52447b9dc139e23b04ccba |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T00:02:31Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a030bcd96d52447b9dc139e23b04ccba2023-11-23T16:14:46ZengMDPI AGElectronics2079-92922022-01-0111330510.3390/electronics11030305Tutorial on the Use of Deep Learning in Diffuse Optical TomographyGanesh M. Balasubramaniam0Ben Wiesel1Netanel Biton2Rajnish Kumar3Judy Kupferman4Shlomi Arnon5Department of Electrical and Computer Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Be’er Sheva 8441405, IsraelDepartment of Electrical and Computer Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Be’er Sheva 8441405, IsraelDepartment of Electrical and Computer Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Be’er Sheva 8441405, IsraelDepartment of Electrical and Computer Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Be’er Sheva 8441405, IsraelDepartment of Electrical and Computer Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Be’er Sheva 8441405, IsraelDepartment of Electrical and Computer Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Be’er Sheva 8441405, IsraelDiffuse optical tomography using deep learning is an emerging technology that has found impressive medical diagnostic applications. However, creating an optical imaging system that uses visible and near-infrared (NIR) light is not straightforward due to photon absorption and multi-scattering by tissues. The high distortion levels caused due to these effects make the image reconstruction incredibly challenging. To overcome these challenges, various techniques have been proposed in the past, with varying success. One of the most successful techniques is the application of deep learning algorithms in diffuse optical tomography. This article discusses the current state-of-the-art diffuse optical tomography systems and comprehensively reviews the deep learning algorithms used in image reconstruction. This article attempts to provide researchers with the necessary background and tools to implement deep learning methods to solve diffuse optical tomography.https://www.mdpi.com/2079-9292/11/3/305diffuse optical tomographyinverse problemsdeep learning |
| spellingShingle | Ganesh M. Balasubramaniam Ben Wiesel Netanel Biton Rajnish Kumar Judy Kupferman Shlomi Arnon Tutorial on the Use of Deep Learning in Diffuse Optical Tomography Electronics diffuse optical tomography inverse problems deep learning |
| title | Tutorial on the Use of Deep Learning in Diffuse Optical Tomography |
| title_full | Tutorial on the Use of Deep Learning in Diffuse Optical Tomography |
| title_fullStr | Tutorial on the Use of Deep Learning in Diffuse Optical Tomography |
| title_full_unstemmed | Tutorial on the Use of Deep Learning in Diffuse Optical Tomography |
| title_short | Tutorial on the Use of Deep Learning in Diffuse Optical Tomography |
| title_sort | tutorial on the use of deep learning in diffuse optical tomography |
| topic | diffuse optical tomography inverse problems deep learning |
| url | https://www.mdpi.com/2079-9292/11/3/305 |
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