Department of Biomedical Engineering and Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE.
Healtcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, UAE.
Eur Radiol Exp. 2024 Jul 1;8(1):73. doi: 10.1186/s41747-024-00472-y.
Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e., ≥ 7 T, in the light of the current growing interest in artificial intelligence (AI) and robot-assisted neurosurgery. The potential of submillimetre fMRI mapping can help better appreciate uncertainty on resection margins, though geometric distortions at UHF might lessen the accuracy of fMRI maps. A useful trade-off for UHF fMRI is to collect data with 1-mm isotropic resolution to ensure high sensitivity and subsequently a low risk of false negatives. Scanning at UHF might yield a revival interest in slow event-related fMRI, thereby offering a richer depiction of the dynamics of fMRI responses. The potential applications of AI concern denoising and artefact removal, generation of super-resolution fMRI maps, and accurate fusion or coregistration between anatomical and fMRI maps. The latter can benefit from the use of T1-weighted echo-planar imaging for better visualization of brain activations. Such AI-augmented fMRI maps would provide high-quality input data to robotic surgery systems, thereby improving the accuracy and reliability of robot-assisted neurosurgery. Ultimately, the advancement in fMRI at UHF would promote clinically useful synergies between fMRI, AI, and robotic neurosurgery.Relevance statement This review highlights the potential synergies between fMRI at UHF, AI, and robotic neurosurgery in improving the accuracy and reliability of fMRI-based presurgical mapping.Key points• Presurgical fMRI mapping at UHF improves spatial resolution and sensitivity.• Slow event-related designs offer a richer depiction of fMRI responses dynamics.• AI can support denoising, artefact removal, and generation of super-resolution fMRI maps.• AI-augmented fMRI maps can provide high-quality input data to robotic surgery systems.
术前功能磁共振成像 (fMRI) 评估可降低术后发病率。在这里,我们结合人工智能 (AI) 和机器人辅助神经外科的日益增长的兴趣,讨论超高磁场 (UHF) 即≥7T 的术前 fMRI 映射。亚毫米 fMRI 映射的潜力可以帮助更好地了解切除边界的不确定性,尽管 UHF 下的几何变形可能会降低 fMRI 图的准确性。UHF fMRI 的一个有用的权衡方案是采集具有 1 毫米各向同性分辨率的数据,以确保高灵敏度和低假阴性风险。在 UHF 下进行扫描可能会重新激发对慢事件相关 fMRI 的兴趣,从而提供 fMRI 响应动力学的更丰富描述。AI 的潜在应用包括去噪和去除伪影、生成超分辨率 fMRI 图,以及在解剖和 fMRI 图之间进行准确的融合或配准。后者可以受益于 T1 加权回波平面成像,以更好地可视化大脑激活。这种 AI 增强的 fMRI 图将为机器人手术系统提供高质量的输入数据,从而提高基于 fMRI 的术前映射的准确性和可靠性。最终,UHF 中的 fMRI 的进步将促进 fMRI、AI 和机器人神经外科之间的临床有用的协同作用。
声明 本文强调了 UHF 中的 fMRI、AI 和机器人神经外科之间的潜在协同作用,以提高基于 fMRI 的术前映射的准确性和可靠性。
关键点
• UHF 下的术前 fMRI 映射可提高空间分辨率和灵敏度。
• 慢事件相关设计提供了 fMRI 响应动力学的更丰富描述。
• AI 可以支持去噪、去除伪影和生成超分辨率 fMRI 图。
• AI 增强的 fMRI 图可以为机器人手术系统提供高质量的输入数据。