目的:由于不自主运动,卒中患者在磁共振成像(MRI)检查中通常面临挑战。本研究旨在通过利用多重重叠回波分离(MOLED)定量技术来克服这些挑战。通过这项技术,我们还试图检测亚急性-慢性卒中患者出现正常皮质脊髓束(NA-CST)的微结构变化.
方法:79例患者接受了3.0TMRI扫描,包括常规扫描和MOLED技术。利用深度学习网络进行图像重建,和准确性,可靠性,并在体模和志愿者上验证了MOLED技术的运动阻力。随后,我们评估了运动功能障碍的严重程度,缺血性病变体积,双侧NA-CST的T2值,以及患者同损和对比损NA-CST之间的T2比率(rT2)。
结果:MOLED技术具有较高的准确性(P<0.001)和出色的可重复性,平均变异系数(CoV)为1.11%。它提供了可靠的定量结果,即使在头部运动,平均差(Meandiff)=0.28%,标准偏差差(SDdiff)=1.34%。此外,同侧NA-CST的T2值明显高于对比侧(P<0.001),rT2与运动功能障碍的严重程度呈正相关(rs=0.575,P<0.001)。此外,rT2成功预测了卒中后运动障碍,曲线下面积(AUC)为0.883。
结论:MOLED技术对于具有不自主运动的卒中患者的定量成像具有显著优势。此外,来自MOLED的T2映射可以检测NA-CST的微观结构变化,可能有助于监测中风引起的运动障碍。
OBJECTIVE: Stroke patients commonly face challenges during magnetic resonance imaging (MRI) examinations due to involuntary movements. This study aims to overcome these challenges by utilizing multiple overlapping-echo detachment (MOLED) quantitative technology. Through this technology, we also seek to detect microstructural changes of the normal-appearing corticospinal tract (NA-CST) in subacute-chronic stroke patients.
METHODS: 79 patients underwent 3.0 T MRI scans, including routine scans and MOLED technique. A deep learning network was utilized for image reconstruction, and the accuracy, reliability, and resistance to motion of the MOLED technique were validated on phantoms and volunteers. Subsequently, we assessed
motor dysfunction severity, ischemic lesion volume, T2 values of the bilateral NA-CST, and the T2 ratio (rT2) between the ipsilesional and contralesional NA-CST in patients.
RESULTS: The MOLED technique showed high accuracy (P < 0.001) and excellent repeatability, with a mean coefficient of variation (CoV) of 1.11%. It provided reliable quantitative results even under head movement, with a mean difference (Meandiff)= 0.28% and a standard deviation difference (SDdiff)= 1.34%. Additionally, the T2 value of the ipsilesional NA-CST was significantly higher than contralesional side (P < 0.001), and a positive correlation was observed between rT2 and the severity of
motor dysfunction (rs =0.575, P < 0.001). Furthermore, rT2 successfully predicted post-stroke
motor impairment, with an area under the curve (AUC) was 0.883.
CONCLUSIONS: The MOLED technique offers significant advantages for quantitatively imaging stroke patients with involuntary movements. Additionally, T2 mapping from MOLED can detect microstructural changes in the NA-CST, potentially aiding in monitoring stroke-induced
motor impairment.