Abstract
Accurate intraoperative soft-tissue deformation modeling is essential for surgical digital twins (SDTs) to evolve AR guidance from a static, preoperative imaging paradigm into a dynamic, deformationaware navigation. However, the predictive value of biomechanical solvers dependsheavilyonpatient-specificmaterialparameteridentification—a persistentworkflowbottleneck.Wepresentanautomated,non-destructive framework for baseline tissue mechanics characterization via stiffnessratio calibration, evaluated on two heterogeneous silicone phantoms with tumor-likeinclusions.Force-displacementdatafromthreeloadingregimes (global compression, local indentation, and regional punching) initialize Hooke’s Law-derived estimates, which are iteratively optimized within a SOFA-based co-rotational finite element simulation until structural stiffness convergence (0.98≤r≤1.02) is reached. Calibrated deformed states are rendered through a headless Blender pipeline using tracked cameratransformationmatrices,producingtemporallysynchronizedvideo- ARoverlayswithoutmanualalignment.Theframeworkconvergedwithin two iterations across all protocols, yielding protocol-specific effective modulus estimates and healthy-tissue overlay accuracy of DSC= 0.94– 0.97. Accounting for a systematic hand-eye calibration offset via a uniformmean cross-correlationcorrection, tumor-regionDSC alignment was recovered to0.51–0.92, confirming that residual calibration offset rather than FEM modeling error was the primary source of misalignment. These resultsestablishareproducible,modularbaselinelinkingphysicalsensing with real-time solvers — a necessary prerequisite for deformation-aware SDT development, pending cross-protocol generalization and in-vivo validation.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/DT4H_035.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=IbRJwL57OY
BibTex
@InProceedings{IriAma_Towards_MICCAISAT2026,
author = { Iribar-Zabala, Amaia AND Pérez-Sánchez, Laura AND Garro-Tejerina, Marcos AND Pascau, Javier AND Beristain-Iraola, Andoni AND García-Sevilla, Mónica},
title = { { Towards surgical digital twins: a soft-tissue phantom study on stiffness-ratio calibration for AR-guided interventions } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17275},
month = {pending},
page = {pending}
}