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Bilateral mandibular lengthening is widely accepted during mandibular distraction osteogenesis. However, distraction osteogenesis are sometimes associated with clinical complications such as open bite deformity, lateral displacement of temporo-mandibular joint, premature consolidation and pin loosening. Although distraction osteogenesis aims to develop pure tensile strain on the regenerate tissue however, in real life situation due to differences in device orientation, materials and misalignment it is often subjected to complex stress and strain regimes. The objective of this study was to characterise the mechanical environment (stress and strain) in the Finite Element Models (FEM) of regenerate tissue during two different device orientations: (a) device placed parallel to the mandibular body (b) device placed parallel to the axis of distraction. Furthermore, the influence of misalignment from above two idealised orientations was also investigated. Methods and Materials: The distraction protocol in this study was similar to previous study of Loboa et al (2005). FE models were developed at four stages: end of latency, distraction day 2, distraction day 5 and distraction day 8. At each time period a distraction of 1mm was applied to the model as it is most widely used distraction rate. In these models two primary distraction vectors were simulated; first when the device is parallel to the body of the mandible and second when the device is parallel to the axis of distraction. Results: indicate that when the device is placed parallel to the mandible the effect of distraction vector variation due to misalignment in transverse plane (±150 at an interval of 50 ; + indicate lateral and – indicates medial) is symmetric and variation in the stress and strain regimes on regenerative tissue are less than 3%. However, when the device is placed parallel to axis of distraction the corresponding change is asymmetric and almost double in magnitude. The greatest differences were seen when misalignment is towards lateral side (+150). Similarly in the sagittal plane variations up to 17% were developed due to 0- 400 change in the distraction vector orientation. Thus the orientation of device which determines the distraction vector plays an important role in determining the mechanical environment around regenerative tissue. The results suggest that implications of misalignment of the device and its sensitivity from the ideal situation should be well understood during clinical planning.

THE EFFECT OF CT SLICE SPACING ON THE GEOMETRY OF 3D MODELS Beat Schmutz*, Martin E. Wullschleger*, Michael A. Schuetz*^ *Queensland University of Technology, Brisbane, Australia ^Princess Alexandra Hospital, Brisbane, Australia INTRODUCTION CT imaging is the current gold standard for the acquisition of morphological data for the subsequent 3D reconstruction of virtual bone models. In addition to clinical diagnostics, CT image-based 3D reconstructions are frequently being used in a broad range of other fields such as kinematic studies (Dennis et al., 2005), finite element modelling (FEM) (Penna et al., 2006; Taddei et al., 2006) and implant design (Robertson et al., 2000). Accurate reconstruction of 3D bone models from CT requires the acquisition of thin and overlapping slices (Hopper et al., 1996; Shin et al., 2002). Such high resolution image data can be obtained from cadaver specimens without any problems. However, in an effort to reduce radiation exposure to the patient thin sections are not always clinically justified. Depending on the application, such data may not always have sufficient image resolution to be useful for research purposes. The current study quantitatively investigates the effect of various image slice spacing’s on the geometry of 3D models of long bones. METHOD The femur of a fresh intact ovine cadaver hind limb was CT scanned at 120KVP and spacing’s of 0.5, 1, 2, 3 and 4 mm for the axial slices. The pixel size was 0.4 x 0.4 mm. The image data was reconstructed using a middle-hard bone kernel and 50% slice overlap. 3D models of the outer contour of the femur were generated using the image processing and registration software Amira 4.1 (Mercury Computer Systems, France). The image data was segmented using a semi-automatic threshold based method. The differences in surface geometry between a model of interest and the reference model (0.5 mm spaced CT data) were quantified with the reverse engineering software RapidForm2006 (INUS Technology). Values were generated and recorded for the mean distance, maximum distance and standard deviation. The percentage fit between a model of interest and the reference model was evaluated within a tolerance of ± 0.5 mm. In addition to quantifying the differences between the entire surfaces of two models their deviations for five anatomical regions (Figure 1) of the bone were also quantified. Figure 1. 3D model with anatomical sections: Distal epiphysis, distal metaphysis, shaft, trochanter region and femur head. RESULTS The effects of the various image slice spacing’s on the geometry of subsequent 3D models are summarized in Table 1. The reference 3D model has been validated against data obtained from a 3D laser scan (Roland, LPX-250) of the dissected bone. CONCLUSIONS For all models the geometric accuracy decreased with an increase in the slice spacing. As expected, the shaft region was the least affected by variations in slice spacing’s. The epiphyseal regions were the most affected due to larger changes in their surface geometry relative to the scan axis. For a tolerance range of ± 0.5 mm, the results indicate that a slice spacing of 1 mm or less is required to accurately reconstruct the articulating surfaces of a long bone. A 2 mm spacing will enable reconstruction of the metaphyseal areas with reasonable accuracy. Whereas a spacing of 3 – 4 mm is sufficient for a reconstruction of the shaft region. This demonstrates that the usefulness of clinically acquired CT data will depend on the accuracy requirements of the application for which the 3D models are intended for. The findings of this study may also be useful in the development of optimal scanning protocols for the imaging of long bones (Zannoni et al. 1998). To extend the validation and applicability of the results of this pilot study, further tests that involve larger samples and different bones will be required. REFERENCES Dennis et al. J. Biomech. 38:241-253, 2005. Pena et al. J. Biomech. 39(9), 1686-1701, 2006. Taddei et al. J. Biomech. 39(13), 2457-2467, 2006. Robertson et al. J. Bone Joint Surg. 82-A(11), 1594-1602, 2000. Hopper et al. J. Comput. Assist. Tomo. 20(5), 841-847, 1996. Shin et al. Korean J. Radiol. 3(1), 49-56, 2002. Zannoni et al. IEEE Trans Med Imag 17(4), 663- 666, 1998. Table 1. Differences in 3D model surface geometries relative to the reference model (0.5 mm spaced CT data) for various CT image slice spacing’s. Model Slice spacing (mm) Ave distance (mm) Max distance (mm) SD Within ± 0.5mm (%) Entire femur 1 0.15 0.97 0.12 98 Entire femur 2 0.28 3.08 0.38 86 Entire femur 3 0.40 4.88 0.50 75 Entire femur 4 0.77 6.83 0.83 52 Dist epiphysis 1 0.22 0.91 0.15 96 Dist epiphysis 2 0.44 3.08 0.52 74 Dist epiphysis 3 0.55 4.72 0.55 61 Dist epiphysis 4 0.95 6.29 0.94 42 Dist meta 1 0.12 0.79 0.07 100 Dist meta 2 0.21 1.86 0.26 93 Dist meta 3 0.26 2.46 0.24 87 Dist meta 4 0.65 3.55 0.60 54 Shaft 1 0.10 0.32 0.05 100 Shaft 2 0.09 0.49 0.07 100 Shaft 3 0.12 0.88 0.11 99 Shaft 4 0.26 1.23 0.18 92 Trochanter 1 0.14 0.85 0.10 99 Trochanter 2 0.21 1.67 0.22 93 Trochanter 3 0.43 2.18 0.67 77 Trochanter 4 0.77 2.92 0.83 49 Femur head 1 0.20 0.97 0.16 96 Femur head 2 0.52 2.36 0.48 63 Femur head 3 0.67 4.88 0.58 51 Femur head 4 1.31 6.83 1.04 27 Dr Beat Schmutz Institute of Health & Biomedical Innovation Queensland University of Technology 60 Musk Avenue Kelvin Grove QLD 4059, Australia Fax: +61 7 3138 6030 Email: b.schmutz@qut.edu.au

Aims Fractures that are not rigidly fixed heal via the formation of a fibro-cartilaginous callus, which is progressively converted to bone. We examine the hypothesis that callus morphology is optimal to achieve a defined limiting stiffness of the bone-callus construct. We demonstrate the use of this principle in designing the starting geometry for iterative computational models of the fracture healing process. Methods For a given fracture geometry, optimal callus dimensions are determined by iteratively increasing the callus size until a defined stiffness is attained, as determined by finite element (FE) analysis of the bone-callus construct. This process is performed using various canonical shapes and material properties for the callus. The optimal morphologies thus predicted are compared to typical histological and radiological observations. The impact of callus morphology on the outcomes of an iterative FE model of fracture healing is investigated using experimental and "optimal" callus dimensions. This model is similar to those published previously, and plots the maturation of tissues in the callus until bony union. We analyse the predicted healing outcomes when optimal, sub-optimal and supra-optimal calluses are used as initial states, by evaluating against existing experimental data. Results Using an FE modelling scheme and comparing its results with typical histology, we establish that callus initially forms an optimally-determined morphology. Secondly, we illustrate the impact of sub-optimal and supra-optimal calluses on the outcomes of an evolutionary FE model, to establish the efficacy of employing optimal callus dimensions in computational models of fracture healing. Conclusions Defining principles for optimal callus morphology provides a feasible starting point for computational models of fracture healing. This may be useful in avoiding modelling artefacts when early-stage experimental data is insufficient. Such models may provide new insights into how different fracture stabilisation methods manipulate the healing process.