Patient-specific virtual surgical planning for tongue reconstruction: evaluating hyperelastic inverse FEM with four simulated tongue cancer cases.

Objective: Anatomically and functionally optimal tongue reconstruction after tumor removal presents significant challenges. Current Virtual Surgical Planning (VSP) utilizes patient-specific data with geometric algorithms for free flap design. However, these geometric approaches often inadequately account for complex soft tissue biomechanics. This study introduces a biomechanics-informed VSP algorithm and computationally compares its flap designs against those derived from purely geometric methods.

Approach: Hyperelastic inverse Finite Element Method (hiFEM) was developed by integrating an Ogden hyperelastic constitutive model into a predecessor algorithm. The planar flap shape is determined by minimizing potential energy when tissue deforms to match patient-specific MRI-derived 3D defect geometry. Four clinically plausible tongue cancer cases were simulated, and resection regions delineated. For each case, flap designs were generated using hiFEM, its predecessor iFEM, and two geometric flattening techniques: NURBS surface flattening and Boundary First Flattening (BFF). Intrinsic tissue deformation for these designs was compared across methods and quantified using area stretch metric.

Main results: Across all simulated cases, hiFEM-generated flap designs required less intrinsic tissue deformation. Maximum area stretch ranged from 1.10-1.12 for hiFEM designs, versus 1.19-1.38 for NURBS flattening and 1.54-1.74 for BFF designs. Furthermore, hiFEM's area stretch distribution was tighter, centered around one (ideal, no stretch). Geometric comparison showed hiFEM yields flap designs similar to the clinically validated geometric algorithm, NURBS flattening, with an average Hausdorff distance of only 1.3 mm. hiFEM's distinct advantage is its core objective of minimizing tissue stretch, which has clinical relevance and suggests potential for improved patient outcomes. Computationally, hiFEM demonstrated robustness and efficiency. It converged rapidly (8 to 10 iterations; less than 0.3s/case), even for complex geometries where iFEM failed.

Significance: hiFEM offers a biomechanically informed and computationally robust tool for tongue VSP, showing potential for broader application in breast, nasal, and other soft tissue reconstructions.