A deep unrolled neural network for real-time MRI-guided brain intervention

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 () and Yuan Feng ()
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Zhao He: Shanghai Jiao Tong University
Ya-Nan Zhu: Shanghai Jiao Tong University
Yu Chen: Shanghai Jiao Tong University
Yi Chen: Shanghai Jiao Tong University
Yuchen He: City University of Hong Kong
Yuhao Sun: Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine
Tao Wang: Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine
Chengcheng Zhang: Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine
Bomin Sun: Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine
Fuhua Yan: Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine
Xiaoqun Zhang: Shanghai Jiao Tong University
Qing-Fang Sun: Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine
Guang-Zhong Yang: Shanghai Jiao Tong University
Yuan Feng: Shanghai Jiao Tong University

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-43966-w

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