Patient-Specific Optimization for Mandibular Reconstruction Planning with Enhanced Bone Union

arXiv cs.CV / 5/5/2026

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Key Points

The study introduces OsteoOpt++, a patient-specific planning loop that turns pre-operative CT data into a personalized digital twin to support mandibular reconstruction with vascularized bone grafts.
OsteoOpt++ uses Bayesian optimization to search for six controllable cut-plane and donor-positioning variables, aiming to maximize donor–mandible apposition while incorporating a safety-factor-regularized objective.
In generic defect scenarios, the method increased cycle-averaged donor–mandible apposition by up to 29 percentage points compared with a common surgical approach, and achieved up to 26 percentage points improvement in patient-specific cases versus a surgeon’s day-5 post-operative configuration.
Sensitivity analysis across multiple modeling parameters showed robust objective behavior (≤3–4% change), and a longitudinal case reported Dice overlap of 0.70–0.76 between predicted apposition and year-1 bone formation.
The authors report that the workflow offers pre-operative, image-driven recommendations for cut-plane orientation and donor placement, and they provide open-source code for optimization and patient-specific modeling.

Abstract

Mandibular reconstruction with vascularized bone grafts is complicated by donor-host nonunion, and current virtual surgical planning produces a geometric plan rather than a configuration that explicitly promotes bone union. We present OsteoOpt++, an image-to-decision planning loop for patient-specific mandibular reconstruction. A pre-operative computed tomography (CT) is converted into a personalized digital twin through template-to-patient registration and CT-derived updates of the muscle and temporomandibular-joint parameters. Bayesian optimization with an expected-improvement-plus acquisition rule then searches six clinically controllable cut-plane and donor-positioning variables under an apposition-driven objective and a safety-factor-regularized variant. The workflow was evaluated on three generic defects (body, symphysis, and ramus-body) and a total of 3+1 patient-specific cases, with 3 used for optimization and 1 for validation. In the generic cases, against a common surgical approach, cycle-averaged donor-mandible apposition increased by up to 29 percentage points (329% relative); in the patient-specific cases, against the surgeon-implemented day-5 post-operative configuration, by up to 26 percentage points. A 10% sensitivity analysis over eleven modeling parameters capped the change in the apposition-driven objective at 3% for generic cases and 4% for patient-specific cases, and the longitudinal case showed Dice overlap of 0.70 and 0.76 between predicted apposition and year-1 bone formation. Clinically, this provides surgeons with a pre-operative, image-driven recommendation for cut-plane orientation and donor placement that is predicted to improve union conditions over the configurations currently delivered in the operating room. The optimization and patient-specific modeling code is open source at https://github.com/hamidreza-aftabi/OsteoOpt.