arXiv:2611.07135v1 Announce Type: new Abstract: Background and Objective: Bridging kidney MRI with patient-specific computational biomechanics may enable accurate, noninvasive assessment of chronic kidney disease. To develop a finite element method (FEM)-based computational elastography framework for estimating renal biomechanical properties from conventional kidney MRI and to evaluate its ability to differentiate chronic kidney disease (CKD) from healthy controls. Methods: Kidney segmentation masks from healthy control and CKD subjects were converted into triangulated surface meshes, and watertight surfaces were tetrahedralized to create volumetric FEM meshes. Kidney tissue was modeled as a nearly incompressible Kelvin-Voigt viscoelastic material. Harmonic wave propagation simulations were performed using FEM, followed by Helmholtz-based elastographic inversion to estimate complex shear modulus distributions. Derived biomechanical parameters were used for CKD classification using machine learning models. Results: FEM-derived stiffness estimates showed consistency with stiffness ranges previously reported in human renal MRE studies and also showed measurable differences between healthy and CKD groups. Machine learning models trained on FEM-derived features achieved classification performance up to 86.26% cross-validation accuracy with an AUC of 0.92. Conclusion: FEM-derived elastographic parameters showed potential for distinguishing CKD from healthy tissue and may provide an alternative for renal biomechanical assessment without dedicated MRE acquisition.