背景:当前的肝脏磁共振弹性成像(MRE)扫描通常需要调整驱动器振幅以产生可接受的图像。这可能导致时间浪费和可能丧失捕获高质量图像的机会。
目的:构建个体化驱动振幅的线性回归模型以提高肝脏MRE图像质量。
方法:来自61名参与者的95次肝脏MRE扫描数据,包括腹部缺失体积比(AMVR),屏气状态,从皮肤表面上的被动驱动器到肝脏边缘的距离(Dd-l),体重指数(BMI),以及被动驾驶员相对于人体矢状平面的横向偏转(角度α),不断收集。采用Spearman相关分析和Lasso回归对自变量进行筛选。建立了多元线性回归方程以确定最佳振幅预测模型。
结果:线性回归模型的最优公式:驾驶员振幅(%)=-16.80+78.59×AMVR-11.12×屏气(呼气末=1,吸气末=0)+3.16×Dd-l+1.94×BMI+0.34×角度α,模型通过F检验(F=22.455,P<0.001),R2值为0.558。
结论:基于AMVR的个性化振幅预测模型,屏气状态,Dd-l,BMI,角度α是肝脏MRE检查的一个有价值的工具。
BACKGROUND: Current liver magnetic resonance elastography (MRE) scans often require adjustments to driver
amplitude to produce acceptable images. This could lead to time wastage and the potential loss of an opportunity to capture a high-quality image.
OBJECTIVE: To construct a linear regression model of individualized driver
amplitude to improve liver MRE image quality.
METHODS: Data from 95 liver MRE scans of 61 participants, including abdominal missing volume ratio (AMVR), breath-holding status, the distance from the passive driver on the skin surface to the liver edge (Dd-l), body mass index (BMI), and lateral deflection of the passive driver with respect to the human sagittal plane (Angle α), were continuously collected. The Spearman correlation analysis and lasso regression were conducted to screen the independent variables. Multiple linear regression equations were developed to determine the optimal
amplitude prediction model.
RESULTS: The optimal formula for linear regression models: driver
amplitude (%) = -16.80 + 78.59 × AMVR - 11.12 × breath-holding (end of expiration = 1, end of inspiration = 0) + 3.16 × Dd-l + 1.94 × BMI + 0.34 × angle α, with the model passing the F test (F = 22.455, P <0.001) and R2 value of 0.558.
CONCLUSIONS: The individualized
amplitude prediction model based on AMVR, breath-holding status, Dd-l, BMI, and angle α is a valuable tool in liver MRE examination.