A deep unrolled neural network for real-time MRI-guided brain intervention
© 2023. The Author(s)..
Accurate navigation and targeting are critical for neurological interventions including biopsy and deep brain stimulation. Real-time image guidance further improves surgical planning and MRI is ideally suited for both pre- and intra-operative imaging. However, balancing spatial and temporal resolution is a major challenge for real-time interventional MRI (i-MRI). Here, we proposed a deep unrolled neural network, dubbed as LSFP-Net, for real-time i-MRI reconstruction. By integrating LSFP-Net and a custom-designed, MR-compatible interventional device into a 3 T MRI scanner, a real-time MRI-guided brain intervention system is proposed. The performance of the system was evaluated using phantom and cadaver studies. 2D/3D real-time i-MRI was achieved with temporal resolutions of 80/732.8 ms, latencies of 0.4/3.66 s including data communication, processing and reconstruction time, and in-plane spatial resolution of 1 × 1 mm2. The results demonstrated that the proposed method enables real-time monitoring of the remote-controlled brain intervention, and showed the potential to be readily integrated into diagnostic scanners for image-guided neurosurgery.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Nature communications - 14(2023), 1 vom: 12. Dez., Seite 8257 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
He, Zhao [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Revised 12.12.2023 published: Electronic Citation Status In-Process |
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| doi: |
10.1038/s41467-023-43966-w |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365775851 |
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| 520 | |a © 2023. The Author(s). | ||
| 520 | |a Accurate navigation and targeting are critical for neurological interventions including biopsy and deep brain stimulation. Real-time image guidance further improves surgical planning and MRI is ideally suited for both pre- and intra-operative imaging. However, balancing spatial and temporal resolution is a major challenge for real-time interventional MRI (i-MRI). Here, we proposed a deep unrolled neural network, dubbed as LSFP-Net, for real-time i-MRI reconstruction. By integrating LSFP-Net and a custom-designed, MR-compatible interventional device into a 3 T MRI scanner, a real-time MRI-guided brain intervention system is proposed. The performance of the system was evaluated using phantom and cadaver studies. 2D/3D real-time i-MRI was achieved with temporal resolutions of 80/732.8 ms, latencies of 0.4/3.66 s including data communication, processing and reconstruction time, and in-plane spatial resolution of 1 × 1 mm2. The results demonstrated that the proposed method enables real-time monitoring of the remote-controlled brain intervention, and showed the potential to be readily integrated into diagnostic scanners for image-guided neurosurgery | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Chen, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a He, Yuchen |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Yuhao |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Tao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Chengcheng |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Bomin |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Fuhua |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiaoqun |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Qing-Fang |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Guang-Zhong |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Yuan |e verfasserin |4 aut | |
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