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MRI 与体感诱发电位相结合有助于诊断脊髓压迫症。

Integration of MRI and somatosensory evoked potentials facilitate diagnosis of spinal cord compression.

机构信息

International Ph.D. Program in Interdisciplinary Neuroscience (UST), College of Biological Science and Technology, National Yang Ming Chiao Tung University, 734, Engineering Bldg. 5, 1001 Daxue Road, Hsinchu, 30010, Taiwan, ROC.

Department of Medical Research, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, 300, Taiwan, ROC.

出版信息

Sci Rep. 2023 May 15;13(1):7861. doi: 10.1038/s41598-023-34832-2.

Abstract

This study aimed to integrate magnetic resonance imaging (MRI) and related somatosensory evoked potential (SSEP) features to assist in the diagnosis of spinal cord compression (SCC). MRI scans were graded from 0 to 3 according to the changes in the subarachnoid space and scan signals to confirm differences in SCC levels. The amplitude, latency, and time-frequency analysis (TFA) power of preoperative SSEP features were extracted and the changes were used as standard judgments to detect neurological function changes. Then the patient distribution was quantified according to the SSEP feature changes under the same and different MRI compression grades. Significant differences were found in the amplitude and TFA power between MRI grades. We estimated three degrees of amplitude anomalies and power loss under each MRI grade and found the presence or absence of power loss occurs after abnormal changes in amplitude only. For SCC, few integrated approach combines the advantages of both MRI and evoked potentials. However, integrating the amplitude and TFA power changes of SSEP features with MRI grading can help in the diagnosis and speculate progression of SCC.

摘要

本研究旨在整合磁共振成像(MRI)和相关体感诱发电位(SSEP)特征,以辅助诊断脊髓压迫症(SCC)。根据蛛网膜下腔和扫描信号的变化,MRI 扫描分为 0 至 3 级,以确认 SCC 水平的差异。提取术前 SSEP 特征的振幅、潜伏期和时频分析(TFA)功率,并将变化作为标准判断,以检测神经功能变化。然后根据相同和不同 MRI 压缩等级下的 SSEP 特征变化对患者分布进行量化。在 MRI 等级之间,发现振幅和 TFA 功率有显著差异。我们在每个 MRI 等级下估计了三种幅度异常程度和功率损失,并发现只有在振幅异常变化后才会出现功率损失的存在或缺失。对于 SCC,很少有综合方法结合 MRI 和诱发电位的优势。然而,整合 SSEP 特征的振幅和 TFA 功率变化与 MRI 分级可以帮助诊断和推测 SCC 的进展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ea4/10185544/017dffcd9063/41598_2023_34832_Fig1_HTML.jpg

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