Optimization of bone fracture treatment

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Source: Dynardo

With increasing life expectancies, the number of femoral fractures among elderly patients due to osteoporosis is expected to rise. To restore mobility in these patients as rapidly as possible, fracture treatment that takes into account the material properties of osteoporotic bone is essential.

The focus of this study was the treatment of femoral shaft fractures. These are commonly treated with plate osteosynthesis, which involves bringing the ends of a fractured bone together and fastening them with a metal plate and screws. Although there are a variety of different plate types, locking compression plates have been widely applied in recent years. Among patients with osteoporosis, the stable fixation of the plate is a big challenge for the surgeon since the bone often lacks the desired stability. This may lead to a high complication rate due to loosening of screws or breakage of the plate.

The aim of this study was to support surgeons in deciding where to place the screws to achieve optimal fracture healing and to prevent implant failure after a femoral shaft fracture. For this purpose, hundreds of different screw arrangements were evaluated and optimized using Dynardo’s optiSLang controlling an automated workflow. The procedure involved:

  • Use of computed tomography data for patient-specific modeling of the inhomogeneous material properties of the bone.
  • Evaluation of biomechanical parameters with finite element analysis.
  • Optimization of the screw arrangement under given constraints.

Optimization constraints included the number of screws, the inter-fragmentary movement, the distance between plate and bone, as well as the yield material properties of bone, plate and screws.

Automating this process offered a whole new perspective compared with currently used approaches for investigating the influence of position and number of screws on fracture healing. Without an automated process, only a small number of different layouts can be evaluated and compared. But the proposed automated system made it possible to select the best layout from hundreds of designs without unreasonable effort.

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Fig. 1: Fracture movement on the near cortex is limited compared with the far cortex. Source: Dynardo

Materials and methods—A partly automated workflow (Fig. 2) was developed to select the best screw arrangement and position for plate osteosynthesis. Some tasks had to be executed manually for every patient, including bone segmentation, repositioning of bone fragments as well as the initial positioning of the plate. The majority of tasks, however, were controlled by optiSLang and performed automatically. These tasks included mesh generation, assignment of material properties and boundary conditions, finite element analysis and optimization. The model consisted of bone fragments, a locking compression plate and a varying number of screws. These objects were either generated or adapted for use in a finite element analysis.

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Fig. 2: Procedure for the optimization of fracture treatment. Source: Dynardo

The CT dataset of a 22-year-old female was supplied by the Department of Radiology at the Technische Universitat Munchen. Materialise’s Mimics software was used for segmentation of the three-dimensional CT data sets. Finally, a three-dimensional geometry of the femur was created. The surface of the bone was smoothed and any small holes, tunnels and peaks on the surface were removed with Geomagic software. In this study, a healthy femur was used as an example, and an artificial transverse fracture with a fracture gap of 3mm was created using the Blender Version 2.67 software (Fig. 3).

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Fig. 3: A transverse fracture with a fracture gap of 3mm was artificially added to a segmented femoral bone. Source: Dynardo

In this simulation, a Locking Compression Plate (LCP, article number 422 258) manufactured by Synthes was used. This plate was designed for treatment of distal femoral fractures. The compression plate had seven screw holes on the distal end and 13 screw holes for fixation along the bone shaft. The plate was designed to match the mean shape of femoral bones of a cohort and allow secure attachment. The plate was positioned relative to the femur following the recommendations of surgeons. The screws were automatically generated using the Blender software at the beginning of each optimization loop. An input file containing information about each screw as a discrete variable was generated automatically by optiSLang. The value “0” represented the state “no screw,” “1” a monocortical screw, and “2” a bicortical screw.

To demonstrate the functionality of this model, four screw designs were chosen. The layouts differed with respect to the bridging length, which is the distance between fracture gap and the first screw on either side of the fracture. The bridging length is known to have a large influence on the stability of the plate-bone construct. Four different designs were evaluated: In design a, the screws were placed directly next to the fracture. In designs b and c, the bridging lengths were composed of two or five unoccupied screws holes respectively. Design d had the largest bridging length, with ten empty screw holes (Fig. 4).

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Fig. 4: Four screw layouts with varying bridging length. The bridging length is the distance between fracture gap and the first screw on either side of the fracture. Source: Dynardo

The finite element mesh was created with ICEM CFD from ANSYS. The mesh was generated (Fig. 5) automatically reading a script in the programming language TCL (Tool Command Language) using the following procedure:

  • Import STL files.
  • Create intersection lines and intersection points between objects.
  • Create material points.
  • Mesh generation.
  • Smoothing of mesh surface.
  • Export data as input file for ANSYS Classic.

Since the arrangement of screws was different for every optimization run, the TCL file was regenerated during every optimization run by a Python program.

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Fig. 5: Meshed bone with plate and screws. Source: Dynardo

The material properties for bone, plate and screws were set in the preprocessor in ANSYS. Plate and screws were considered as homogeneous materials made of either the titanium alloy Ti-6Al-7Nb or stainless steel 316L. Bone was modeled as an inhomogeneous material consisting of 72 different materials depending on the HU value of the element. Cortical bone properties were chosen as the material property for the homogeneous bone to assess the difference between modeling the bone as a homogeneous object compared with an inhomogeneous object. All materials were assumed to be linear elastic and isotropic. Patient-specific bone material properties, such as Young’s modulus, were derived from Hounsfield Units contained in CT data. The mechanical properties were mapped onto the mesh using an algorithm programmed in Python.

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Fig. 6: Physiological loading case—force application from distal. Source: Dynardo
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Fig. 7: Determination of inter-fragmentary movement using the relative displacement of two points on the far cortex and on the near cortex of the bone. (Pp,xxx = Displacement of node on proximal bone fragment, Pd,xxx = Displacement of node on distal bone fragment, d = Relative displacement between two points). Source: Dynardo
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Fig. 8: Assigning HU as temperatures on the finite element model of bone. Left side: CT scan (top) and finite element model with HU distribution (bottom) of femoral shaft bone. Right side: Two parallel longitudinal sections of the finite element model of femoral bone. Cortical bone (red) was mainly located along the bone shaft. The femoral epiphyses primarily consisted of cancellous bone (green). The dark blue array of the bone indicated the bone marrow. No HU were assigned to the plate and screws. Source: Dynardo

Optimization—The sensitivity analysis and optimization were performed using the software optiSLang v4 (Fig. 9). New designs were created using an evolutionary algorithm. The objective of the optimization was to minimize the number of screws. The parametric model consisted of 21 design parameters. Twenty design parameters were responsible for generating the screws. These parameters could take on one of three discrete states: 0 representing no screw, 1 representing a monocortical screw and 2 representing a bicortical screw. As the subject bone showed no signs of osteoporosis, the same outcomes would be expected no matter whether monocortical or bicortical screws were applied. Therefore, only two discrete states were permitted (0 = no screw, 2 = bicortical screw). The remaining design parameter represented the distance between plate and bone. This parameter could take on a continuous value between 0mm and 5mm.

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Fig. 9: Upper panel: sensitivity workflow with metamodeling. Middle panel: optimization workflow. Lower panel: solver chain. Source: Dynardo

The optimization constraints were chosen according to findings from the literature. To maintain a stable attachment of the plate to the bone fragments, at least two screws had to be placed in every fragment. The inter-fragmentary movement was considered an important boundary condition for optimizing the number of screws and their position. Due to the bending load, the inter-fragmentary movement on the near cortex was generally smaller than on the far cortex. Two constraints were specified to take this behavior into account. To prevent failure of the implant under overloading the stress levels in the bone, plate and screws had to remain below the yield stress for the corresponding material. Overload was defined as three times the recommended load.

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Fig. 10: Relationship between bridging length and inter-fragmentary movement on the far cortex. A linear correlation between the bridging length and the relative movement of the far cortex was observed. Source: Dynardo

The bridging length had a significant impact on the inter-fragmentary movement. A larger bridging length resulted in a linear increase in relative movement on the near cortex (Fig. 10). A positive linear relationship between bridging length and relative movement was observed on the far cortex. The objective of the optimization was to minimize the total number of screws. A design with four screws was selected as the optimal design by the evolutionary optimization algorithm (Fig. 11). The design had a medium bridging length of four unoccupied screw holes which equaled a distance of 100mm. There was a 0.5mm distance between plate and bone. A total of 180 designs were evaluated in order to select the best design. Designs with up to 16 screws were evaluated during the random sampling period. Primarily designs with four screws were tested toward the end of the optimization.

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Fig. 11: Optimal design after optimization under predefined constraints. The optimal design consisted of four screws in total, two screws on the distal segment and two screws on the proximal segment. The bridging length measured 100mm. Source: Dynardo

Results—This study developed a general procedure for the optimization of fracture treatment. The aim was to improve the healing process by determining the optimal screw configuration under certain biomechanical constraints. The developed workflow enabled the selection of an optimal screw layout out of several thousand possible arrangements. For this purpose, the finite element mesh generation and the finite element analysis were successfully automated. This is the first procedure which allows for more than the comparison between individual FEAs of different plate osteosynthesis.

The optimization process required minimal user input. The user only needed to segment the bone, position the plate relative to the bone and select a couple of specific points on the model. These points included the material points of bone and plate, the measurement points for the inter-fragmentary movement as well as the points for force application and constraints. In future, user input may be further reduced by an automated segmentation procedure.

The rest of the procedure was performed automatically through a batch file. The selection of an optimal design, based on more than 150 other designs, using an evolutionary algorithm was completed within 24 hours. Additional parallelization of the computation process may be able to further decrease this computation time.

Read the full case study: “Optimization of Fracture Treatment,” M. Schimmelpfennig (Dynardo GmbH); C. Wittkowske, S. Raith, J. Jalali, A. Volf, L. Kovacs (Research Group CAPS, TU Munchen); A. Nolte (CADFEM GmbH); B. Konig, S. Dobele (Berufsgenossenschaftliche Unfallklinik Tubingen); J. Bauer, E. Grande Gracia (Institut fur diagnostische und interventionelle Radiologie der TU Munchen).

Aerodynamic optimization of a high-speed train

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Source: Optimal Solutions Software

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Seeking a more efficient engineering approach that would let it maximize the number of designs that could be tested within the project schedule, the manufacturer selected Sculptor from Optimal Solutions Software. By specifying key design parameters, the software allows hundreds of designs to be generated in a matter of minutes—a radical improvement over the company’s previous process, in which this phase of the project took months. Continue reading

Multi-objective optimization of a motorcycle composite swing-arm

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Motorbike swing-arm. Source: iChrome

Composite materials are rapidly supplanting metals in racing and sport vehicles, providing comparable strength and stiffness at much lower weight. This project was a feasibility study to replace the single-sided swing-arm in MV Agusta’s high-performance F4 1000R and Brutale 990R/1090RR motorcycles, originally made of aluminum alloy, with a new design consisting of resin transfer molding (RTM) carbon composite. Injection-based technologies for long-fiber reinforcement such as RTM have proven particularly effective for motorsport cars and motorbikes. Continue reading

Hull form optimization of a mega-boxer

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MSC Danit. Source: DSME

With a capacity of 14,000 TEUs, an overall length of 365.5 meters and a deadweight of 165,517 metric tons, the MSC Danit became the world’s largest container vessel when finished by Korean shipyard Daewoo Shipbuilding & Marine Engineering (DSME). A New Panamax design, the ship offers excellent performance despite its record size. DSME used FRIENDSHIP SYSTEMSCAESES / FRIENDSHIP-Framework (FFW) for variation and optimization of proven parent ships to arrive at a design with maximum performance and efficiency. The final hull displayed less than 50% of the wave resistance of the baseline design. In addition, the hydrodynamic optimization in CAESES/FFW had a favorable effect on propulsion performance. Continue reading

Aerodynamic and structural optimization of a turbofan

Optimizing turbofan designs is challenging because of the need to deal with both aerodynamic and structural performance. While designing a fan with high performance and efficiency is important, strength and reliability of the structure are also critical. Further, integrating parametric CAD, CFD and stress analysis is not a trivial task and usually takes extensive effort.

In this project SmartDO and ANSYS were utilized together to optimize a turbofan. The technical services team at SmartDO’s developer, FEA-Opt Technology, helped the customer build a parametric model with ANSYS DesignModeler, construct the CFX and mechanical analysis block, and integrate SmartDO with ANSYS Workbench. SmartDO was able to increase the mass flow of the fan by almost 14% while slightly reducing the maximum stress under aerodynamic and structural loading. These results showed promising possibilities for further design development. Continue reading

Multidisciplinary optimization of ground vehicle dynamics

Pratt & Miller Engineering evolved from a small business focused on designing and building race cars into a full-service engineering and low-volume manufacturing company serving a global customer base in the defense, automotive and powersports industries. After evaluating multiple optimization tools, Pratt & Miller selected Red Cedar Technology’s HEEDS MDO and its SHERPA algorithm as the only optimization technology that could solve its highly constrained models. Continue reading

MBSE—Platform to accelerate adoption of design space exploration?

These issues will be the focus of my Analyst Briefing session at COFES 2015, Scottsdale, Arizona, April 16-19. Hope to see you there!

Design space exploration, design optimization and process integration technologies are creating value for manufacturers around the world, delivering results that weren’t practical or even possible with older tools and methods. Yet few of these vendors have matched the robust double-digit growth of mainstream CAE in recent years. Can the move by engineering organizations toward model-based systems engineering (MBSE) serve as a platform to accelerate adoption of design space exploration? Continue reading

Design space exploration: Institutionalizing the practice

EVENT NOTICE—If you’re in the Detroit area, join Noesis Solutions’ free seminar April 9 on Innovations in Process Integration & Design Optimization. You’ll hear three perspectives on how this technology can help you streamline your simulation processes and identify benchmark product designs in less time, including a presentation by me on Innovative Uses of Optimization for Engineering Design Problems. Event and registration information here.

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Achieving institutional adoption of design space exploration, design optimization and process integration is a crucial goal for engineering organizations working to establish these as strategic competencies, not just tactical. Too often implemented at only the department or workgroup level, the technologies and their attendant work processes need to be recognized and given backing as enterprise capabilities to have their greatest impact on engineering’s ability to advance corporate strategic objectives. [See also Design space exploration: Justifying the investment.] Continue reading

Ford adopts ESTECO’s SOMO to institutionalize enterprise MDO

Ford Motor Company adopted ESTECO’s SOMO as a tool to enable what it terms an enterprise multidisciplinary design optimization (EMDO) system, announced Dr. Yan Fu, technical leader of business strategy and engineering optimization at Ford. SOMO is an enterprise collaboration and distributed execution framework developed by ESTECO and customized to meet Ford’s engineering processes and IT requirements.

Ford’s move affirms two key points we have long observed:

  • MDO is a strategic capability for engineering organizations, not just tactical.
  • To have strategic impact, MDO needs to be institutionalized at the enterprise level, not implemented as just a departmental or workgroup capability.

Continue reading

Geometry optimization of a static helix mixer

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Source: DATADVANCE

Static helix mixers are widely used in the chemical industry for in-line blending of liquids under laminar flow conditions. Geometric modification of their elements can yield significant improvements in mixing performance. In a project for Sulzer Mixpac, a leading provider of mixer technologies, DATADVANCE determined the optimal geometric parameters for a helix mixer that yield minimal pressure drop together with best mixing performance. Continue reading

Technology business strategy for 21st-century engineering practice