Semi-automated digital workflow to design and evaluate patient-specific mandibular reconstruction implants.

The reconstruction of large mandibular defects with optimal aesthetic and functional outcomes remains a major challenge for maxillofacial surgeons. The aim of this study was to design patient-specific mandibular reconstruction implants through a semi-automated digital workflow and to assess the effects of topology optimization on the biomechanical performance of the designed implants. By using the proposed workflow, a fully porous implant (LA-implant) and a topology-optimized implant (TO-implant) both made of Ti-6Al-4V ELI were designed and additively manufactured using selective laser melting. The mechanical performance of the implants was predicted by performing finite element analysis (FEA) and was experimentally assessed by conducting quasi-static and cyclic biomechanical tests. Digital image correlation (DIC) was used to validate the FE model by comparing the principal strains predicted by the FEM model with the measured distribution of the same type of strain. The numerical predictions were in good agreement with the DIC measurements and the predicted locations of specimen failure matched the actual ones. No statistically significant differences (p < 0.05) in the mean stiffness, mean ultimate load, or mean ultimate displacement were detected between the LA- and TO-implant groups. No implant failures were observed during quasi-static or cyclic testing under masticatory loads that were substantially higher (>1000 N) than the average maximum biting force of healthy individuals. Given its relatively lower weight (16.5%), higher porosity (17.4%), and much shorter design time (633.3%), the LA-implant is preferred for clinical application. This study clearly demonstrates the capability of the proposed workflow to develop patient-specific implants with high precision and superior mechanical performance, which will greatly facilitate cost- and time-effective pre-surgical planning and is expected to improve the surgical outcome.

Finite element analysis of different loading conditions for implant-supported overdentures supported by conventional or mini implants.

The effect of implants' number on overdenture stability and stress distribution in edentulous mandible, implants and overdenture was numerically investigated for implant-supported overdentures. Three models were constructed. Overdentures were connected to implants by means of ball head abutments and rubber ring. In model 1, the overdenture was retained by two conventional implants; in model 2, by four conventional implants; and in model 3, by five mini implants. The overdenture was subjected to a symmetrical load at an angle of 20 degrees to the overdenture at the canine regions and vertically at the first molars. Four different loading conditions with two total forces (120, 300 N) were considered for the numerical analysis. The overdenture displacement was about 2.2 times higher when five mini implants were used rather than four conventional implants. The lowest stress in bone bed was observed with four conventional implants. Stresses in bone were reduced by 61% in model 2 and by 6% in model 3 in comparison to model 1. The highest stress was observed with five mini implants. Stresses in implants were reduced by 76% in model 2 and 89% increased in model 3 compared to model 1. The highest implant displacement was observed with five mini implants. Implant displacements were reduced by 29% in model 2, and increased by 273% in model 3 compared to model 1. Conventional implants proved better stability for overdenture than mini implants. Regardless the type and number of implants, the stress within the bone and implants are below the critical limits.

TLEM 2.0 - a comprehensive musculoskeletal geometry dataset for subject-specific modeling of lower extremity.

When analyzing complex biomechanical problems such as predicting the effects of orthopedic surgery, subject-specific musculoskeletal models are essential to achieve reliable predictions. The aim of this paper is to present the Twente Lower Extremity Model 2.0, a new comprehensive dataset of the musculoskeletal geometry of the lower extremity, which is based on medical imaging data and dissection performed on the right lower extremity of a fresh male cadaver. Bone, muscle and subcutaneous fat (including skin) volumes were segmented from computed tomography and magnetic resonance images scans. Inertial parameters were estimated from the image-based segmented volumes. A complete cadaver dissection was performed, in which bony landmarks, attachments sites and lines-of-action of 55 muscle actuators and 12 ligaments, bony wrapping surfaces, and joint geometry were measured. The obtained musculoskeletal geometry dataset was finally implemented in the AnyBody Modeling System (AnyBody Technology A/S, Aalborg, Denmark), resulting in a model consisting of 12 segments, 11 joints and 21 degrees of freedom, and including 166 muscle-tendon elements for each leg. The new TLEM 2.0 dataset was purposely built to be easily combined with novel image-based scaling techniques, such as bone surface morphing, muscle volume registration and muscle-tendon path identification, in order to obtain subject-specific musculoskeletal models in a quick and accurate way. The complete dataset, including CT and MRI scans and segmented volume and surfaces, is made available at http://www.utwente.nl/ctw/bw/research/projects/TLEMsafe for the biomechanical community, in order to accelerate the development and adoption of subject-specific models on large scale. TLEM 2.0 is freely shared for non-commercial use only, under acceptance of the TLEMsafe Research License Agreement.

Evaluation of a morphing based method to estimate muscle attachment sites of the lower extremity.

To generate subject-specific musculoskeletal models for clinical use, the location of muscle attachment sites needs to be estimated with accurate, fast and preferably automated tools. For this purpose, an automatic method was used to estimate the muscle attachment sites of the lower extremity, based on the assumption of a relation between the bone geometry and the location of muscle attachment sites. The aim of this study was to evaluate the accuracy of this morphing based method. Two cadaver dissections were performed to measure the contours of 72 muscle attachment sites on the pelvis, femur, tibia and calcaneus. The geometry of the bones including the muscle attachment sites was morphed from one cadaver to the other and vice versa. For 69% of the muscle attachment sites, the mean distance between the measured and morphed muscle attachment sites was smaller than 15 mm. Furthermore, the muscle attachment sites that had relatively large distances had shown low sensitivity to these deviations. Therefore, this morphing based method is a promising tool for estimating subject-specific muscle attachment sites in the lower extremity in a fast and automated manner.