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.
Purpose: To construct a linear regression model of individualized driver amplitude to improve liver MRE image quality.
Material and 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.
Conclusion: 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.
Keywords: Liver; amplitude; individualized; magnetic resonance elastography; precision.