Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissionoup.com..
Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:31 |
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| Enthalten in: |
Cerebral cortex (New York, N.Y. : 1991) - 31(2021), 1 vom: 01. Jan., Seite 463-482 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tian, Qiyuan [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 10.01.2022 Date Revised 16.02.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/cercor/bhaa237 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM314607676 |
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| 245 | 1 | 0 | |a Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution |
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| 520 | |a © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissionoup.com. | ||
| 520 | |a Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a anatomical magnetic resonance imaging | |
| 650 | 4 | |a convolutional neural network | |
| 650 | 4 | |a cortical surface reconstruction | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a super-resolution | |
| 700 | 1 | |a Bilgic, Berkin |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Qiuyun |e verfasserin |4 aut | |
| 700 | 1 | |a Ngamsombat, Chanon |e verfasserin |4 aut | |
| 700 | 1 | |a Zaretskaya, Natalia |e verfasserin |4 aut | |
| 700 | 1 | |a Fultz, Nina E |e verfasserin |4 aut | |
| 700 | 1 | |a Ohringer, Ned A |e verfasserin |4 aut | |
| 700 | 1 | |a Chaudhari, Akshay S |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Yuxin |e verfasserin |4 aut | |
| 700 | 1 | |a Witzel, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Setsompop, Kawin |e verfasserin |4 aut | |
| 700 | 1 | |a Polimeni, Jonathan R |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Susie Y |e verfasserin |4 aut | |
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