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Table 4 Summary of input parameters used in FSI models and prediction methods

From: Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid–structure interaction models and machine learning methods with patient follow-up data: a feasibility study

Image Resolution (μm) Image size Field of view (mm2) Pixel size (mm)
Image     
 IVUS 150–200 512*512 9*9 0.01752
 OCT 10–20 704*704 7.01*7.01 0.00996
 Angiography  > 200 512*512 152*152 0.29688
Model parameters c1 (kPa) c2 (kPa) D1 (kPa) D2 K (kPa) K1 (kPa) K2
Material        
 Tissue − 278.7 24.35 133.7 2 13,157 7.19 23.5
 Lipid 0.5 0 0.5 1.5 1250
 Calcification 92 0 36 2 164,000
Pressures Maximum = 136 mmHg, Minimum = 88 mmHg
Prediction methods Setting Data processing Validation
SVM Kernel function: Gaussian radial basis function Synthetic minority oversampling technique (SMOTE) Fivefold cross-validation
RF Number of tree: 20
EL Number of ensemble learning cycles: 100
DA Discriminant type: linear