Entenmann Christian J, Kersting Katharina, Vajkoczy Peter, Zdunczyk Anna
Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Department of Neurosurgery, Berlin, Germany.
Brain Spine. 2025 May 15;5:104283. doi: 10.1016/j.bas.2025.104283. eCollection 2025.
Conventional MRI (T1 and T2-weighted sequences) is the standard for diagnosing spinal cord injuries but often lacks specificity, showing limited correlation with microstructural changes and function. This creates a diagnostic gap, especially in patients with mild or ambiguous symptoms, delaying early intervention.
Can advanced MRI techniques-such as quantitative MRI (qMRI), functional MRI (fMRI), Magnetic Resonance Spectroscopy (MRS), and Transmagnetic Stimulation (TMS)-address the limitations of conventional MRI by providing enhanced diagnostic metrics and biomarkers of spinal cord integrity?
This study reviews advanced MRI modalities and their potential to provide quantifiable insights into spinal cord microstructure and function. It also explores the role of artificial intelligence (AI) in analyzing complex datasets to support more comprehensive diagnostics.
Advanced MRI techniques show promise in improving diagnostic accuracy and enabling individualized prognostic assessments. Parameters specific to each modality could serve as biomarkers for injury extent and neurological recovery, supporting their potential as clinical endpoints in therapy trials.
These advanced imaging techniques, combined with AI for data integration, offer a transformative potential for personalized diagnostics in spinal cord injury. Yet, significant technical and validation challenges remain, requiring large, multicenter studies to confirm their effectiveness and enable clinical application. Successfully addressing these challenges could close the diagnostic gap, optimize patient outcomes, and redefine spinal cord injury management.
传统的磁共振成像（T1加权和T2加权序列）是诊断脊髓损伤的标准方法，但往往缺乏特异性，与微观结构变化和功能的相关性有限。这就造成了诊断上的差距，尤其是在症状轻微或不明确的患者中，会延迟早期干预。
先进的磁共振成像技术，如定量磁共振成像（qMRI）、功能磁共振成像（fMRI）、磁共振波谱（MRS）和经磁刺激（TMS），能否通过提供增强的诊断指标和脊髓完整性生物标志物来解决传统磁共振成像的局限性？
本研究回顾了先进的磁共振成像模式及其对脊髓微观结构和功能提供可量化见解的潜力。它还探讨了人工智能（AI）在分析复杂数据集以支持更全面诊断方面的作用。
先进的磁共振成像技术在提高诊断准确性和实现个性化预后评估方面显示出前景。每种模式特有的参数可作为损伤程度和神经恢复的生物标志物，支持它们作为治疗试验临床终点的潜力。
这些先进的成像技术，结合人工智能进行数据整合，为脊髓损伤的个性化诊断提供了变革性潜力。然而，重大的技术和验证挑战仍然存在，需要大型多中心研究来证实其有效性并实现临床应用。成功应对这些挑战可以弥合诊断差距，优化患者预后，并重新定义脊髓损伤的管理。
