Huang Yi-Jhe, Chai Jyh-Wen, Chen Wen-Hsien, Chen Hung-Chieh, Cheng Da-Chuan
Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404328, Taiwan.
Department of Radiology, Taichung Veterans General Hospital, Taichung 407219, Taiwan.
Diagnostics (Basel). 2025 Sep 15;15(18):2339. doi: 10.3390/diagnostics15182339.
: Spontaneous intracranial hypotension (SIH), caused by spinal cerebrospinal fluid (CSF) leakage, commonly presents with orthostatic headache and CSF hypovolemia. While CSF dynamics in the cerebral aqueduct are well studied, alterations in spinal CSF flow remain less defined. We aimed to quantitatively assess spinal CSF flow at C2 using phase-contrast (PC) MRI enhanced by artificial intelligence (AI) and to evaluate its utility for diagnosing SIH and predicting responses to epidural blood patch (EBP). : We enrolled 31 patients with MRI-confirmed SIH and 26 age- and sex-matched healthy volunteers (HVs). All participants underwent ECG-gated cine PC-MRI at the C2 level and whole-spine MR myelography. AI-based segmentation using YOLOv4 and a pulsatility-based algorithm was used to extract quantitative CSF flow metrics. Between-group comparisons were analyzed using Mann-Whitney U tests, and receiver operating characteristic (ROC) analysis was used to evaluate diagnostic and predictive performance. : Compared to HVs, SIH patients showed significantly reduced CSF flow parameters across all metrics, including upward/downward mean flow, peak flow, total flow per cycle, and absolute stroke volume (all < 0.001). ROC analysis revealed excellent diagnostic accuracy for multiple parameters, particularly downward peak flow (AUC = 0.844) and summation of peak flow (AUC = 0.841). Importantly, baseline CSF flow metrics significantly distinguished patients who required one versus multiple epidural blood patches (EBPs) (all < 0.001). ROC analysis demonstrated that several parameters achieved near-perfect to perfect accuracy in predicting EBP success, with AUCs up to 1.0 and 100% sensitivity/specificity. : AI-enhanced PC-MRI enables the robust, quantitative evaluation of spinal CSF dynamics in SIH. These flow metrics not only differentiate SIH patients from healthy individuals but also predict response to EBP treatment with high accuracy. Quantitative CSF flow analysis may support both diagnosis and personalized treatment planning in SIH.
自发性颅内低压（SIH）由脊髓脑脊液（CSF）漏引起，通常表现为体位性头痛和脑脊液容量减少。虽然大脑导水管中的脑脊液动力学已得到充分研究，但脊髓脑脊液流动的改变仍不太明确。我们旨在使用人工智能（AI）增强的相位对比（PC）MRI定量评估C2水平的脊髓脑脊液流动，并评估其在诊断SIH和预测硬膜外血贴（EBP）反应方面的效用。我们招募了31例MRI确诊的SIH患者和26名年龄及性别匹配的健康志愿者（HV）。所有参与者均在C2水平接受心电图门控电影PC-MRI和全脊柱磁共振脊髓造影。使用基于YOLOv4的人工智能分割和基于搏动性的算法来提取定量脑脊液流动指标。组间比较采用Mann-Whitney U检验，受试者操作特征（ROC）分析用于评估诊断和预测性能。与HV相比，SIH患者在所有指标上的脑脊液流动参数均显著降低，包括向上/向下平均流量、峰值流量、每个周期的总流量和绝对搏出量（均P<0.001）。ROC分析显示多个参数具有出色的诊断准确性，尤其是向下峰值流量（AUC = 0.844）和峰值流量总和（AUC = 0.841）。重要的是，基线脑脊液流动指标能显著区分需要一次与多次硬膜外血贴（EBP）的患者（均P<0.001）。ROC分析表明，几个参数在预测EBP成功方面达到了近乎完美至完美的准确性，AUC高达1.0，灵敏度/特异性为100%。AI增强的PC-MRI能够对SIH患者的脊髓脑脊液动力学进行可靠的定量评估。这些流动指标不仅能将SIH患者与健康个体区分开来，还能高精度地预测对EBP治疗的反应。定量脑脊液流动分析可能有助于SIH的诊断和个性化治疗规划。
