A deep unrolled neural network for real-time MRI-guided brain intervention
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-43966-w |
| _version_ | 1797388190726750208 |
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| author | Zhao He Ya-Nan Zhu Yu Chen Yi Chen Yuchen He Yuhao Sun Tao Wang Chengcheng Zhang Bomin Sun Fuhua Yan Xiaoqun Zhang Qing-Fang Sun Guang-Zhong Yang Yuan Feng |
| author_facet | Zhao He Ya-Nan Zhu Yu Chen Yi Chen Yuchen He Yuhao Sun Tao Wang Chengcheng Zhang Bomin Sun Fuhua Yan Xiaoqun Zhang Qing-Fang Sun Guang-Zhong Yang Yuan Feng |
| author_sort | Zhao He |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-08T22:37:11Z |
| format | Article |
| id | doaj.art-369df190522a425793491f10c1d10c08 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-08T22:37:11Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-369df190522a425793491f10c1d10c082023-12-17T12:22:33ZengNature PortfolioNature Communications2041-17232023-12-0114111210.1038/s41467-023-43966-wA deep unrolled neural network for real-time MRI-guided brain interventionZhao He0Ya-Nan Zhu1Yu Chen2Yi Chen3Yuchen He4Yuhao Sun5Tao Wang6Chengcheng Zhang7Bomin Sun8Fuhua Yan9Xiaoqun Zhang10Qing-Fang Sun11Guang-Zhong Yang12Yuan Feng13School of Biomedical Engineering, Shanghai Jiao Tong UniversitySchool of Mathematical Sciences, MOE-LSC and Institute of Natural Sciences, Shanghai Jiao Tong UniversitySchool of Biomedical Engineering, Shanghai Jiao Tong UniversitySchool of Biomedical Engineering, Shanghai Jiao Tong UniversityDepartment of Mathematics, City University of Hong KongDepartment of Neurosurgery, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Neurosurgery, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Neurosurgery, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Neurosurgery, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Radiology, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of MedicineSchool of Mathematical Sciences, MOE-LSC and Institute of Natural Sciences, Shanghai Jiao Tong UniversityDepartment of Neurosurgery, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of MedicineSchool of Biomedical Engineering, Shanghai Jiao Tong UniversitySchool of Biomedical Engineering, Shanghai Jiao Tong UniversityAbstract 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.https://doi.org/10.1038/s41467-023-43966-w |
| spellingShingle | Zhao He Ya-Nan Zhu Yu Chen Yi Chen Yuchen He Yuhao Sun Tao Wang Chengcheng Zhang Bomin Sun Fuhua Yan Xiaoqun Zhang Qing-Fang Sun Guang-Zhong Yang Yuan Feng A deep unrolled neural network for real-time MRI-guided brain intervention Nature Communications |
| title | A deep unrolled neural network for real-time MRI-guided brain intervention |
| title_full | A deep unrolled neural network for real-time MRI-guided brain intervention |
| title_fullStr | A deep unrolled neural network for real-time MRI-guided brain intervention |
| title_full_unstemmed | A deep unrolled neural network for real-time MRI-guided brain intervention |
| title_short | A deep unrolled neural network for real-time MRI-guided brain intervention |
| title_sort | deep unrolled neural network for real time mri guided brain intervention |
| url | https://doi.org/10.1038/s41467-023-43966-w |
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