Journal of Biomechanics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
Contents lists available at ScienceDirect
Journal of Biomechanics journal homepage: www.elsevier.com/locate/jbiomech www.JBiomech.com
Short communication
Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries Madelen Fahlstedt a,n, Bart Depreitere b, Peter Halldin a, Jos Vander Sloten c, Svein Kleiven a a
Neuronic Engineering, School of Technology and Health, KTH Royal Institute of Technology, Alfred Nobels Allé 10, 141 52 Huddinge, Sweden Experimental Neurosurgery and Neuroanatomy, KU Leuven, Belgium c Biomechanics, KU Leuven, Belgium b
art ic l e i nf o Article history: Accepted 28 February 2015 Keywords: Head injuries Accident reconstructions Image correlation Bicycle accidents FEA
Abstract: At present, Finite Element (FE) analyses are often used as a tool to better understand the mechanisms of head injury. Previously, these models have been compared to cadaver experiments, with the next step under development being accident reconstructions. Thus far, the main focus has been on deriving an injury threshold and little effort has been put into correlating the documented injury location with the response displayed by the FE model. Therefore, the purpose of this study was to introduce a novel image correlation method that compares the response of the FE model with medical images. The injuries shown on the medical images were compared to the strain pattern in the FE model and evaluated by two indices; the Overlap Index (OI) and the Location Index (LI). As the name suggests, OI measures the area which indicates both injury in the medical images and high strain values in the FE images. LI evaluates the difference in center of mass in the medical and FE images. A perfect match would give an OI and LI equal to 1. This method was applied to three bicycle accident reconstructions. The reconstructions gave an average OI between 0.01 and 0.19 for the three cases and between 0.39 and 0.88 for LI. Performing injury reconstructions are a challenge as the information from the accidents often is uncertain. The suggested method evaluates the response in an objective way which can be used in future injury reconstruction studies. & 2015 Elsevier Ltd. All rights reserved.
1. Introduction Traumatic Brain Injuries (TBI) result in approximately 53,000 fatalities annually in the US (Coronado et al., 2011). Fall accidents, traffic accidents and firearms are the main causes of fatal TBI (Coronado et al., 2011). To reduce the number of TBI, more knowledge is needed to understand the mechanism of injury. During the last decades, Finite Element (FE) head models have become a more common tool for studying the mechanism of head injuries (Doorly, 2007; Kleiven, 2006; Mao et al., 2013; McAllister et al., 2012; Takhounts et al., 2008; Willinger and Baumgartner, 2003; Zhang et al., 2004). Most models are compared against cadaver experiments involving intra-cranial pressure (Nahum et al., 1977), intra-cerebral acceleration (Trosseille et al., 1992) and relative motions (Hardy et al., 2001). The next step has been to compare the models against real accidents to determine thresholds for injury and non-injury situations. Until now, the accident reconstructions have mainly been focused on deriving mean values or probability values for injury metrics and
thresholds especially for concussion, while few studies have attempted to correlate injury location. Some studies have tried to visually compare the medical images and FE simulations (Anderson, 1999; Dokko et al., 2003; Fahlstedt et al., 2012; Kleiven, 2007; Mao and Yang, 2011) but few studies have tried to quantify the correlation of injury location and volume (Doorly, 2007; Mao and Yang, 2011; Post et al., 2015). The use of accident reconstructions to evaluate FE models and, in turn, using them to better understand the mechanism of the head injury is becoming increasingly prevalent. To be able to fully use the models in accident reconstruction not only the threshold for injury needs to be evaluated but also the location of injury. No objective, statistics-based image correlation method has previously been used to correlate injury patterns from real-life TBI with output from FE analysis. Therefore, the purpose of this study was to introduce a novel image correlation method that compares the response of the FE model with medical images and applies this to FE bicycle accident reconstructions.
2. Methods n
Corresponding author. Tel.: þ 46 87904876; fax: þ 46 8 21 83 68. E-mail address: [email protected] (M. Fahlstedt).
In this study the novel image correlation method between FE simulations and medical images was evaluated on three single bicycle accident cases collected by
http://dx.doi.org/10.1016/j.jbiomech.2015.02.057 0021-9290/& 2015 Elsevier Ltd. All rights reserved.
Please cite this article as: Fahlstedt, M., et al., Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries. Journal of Biomechanics (2015), http://dx.doi.org/10.1016/j.jbiomech.2015.02.057i
M. Fahlstedt et al. / Journal of Biomechanics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
2
KU Leuven (Table 1). The accident reconstructions of the head injuries were performed with the KTH Royal Institute of Technology head model (Kleiven, 2007), slightly modified as described in Appendix A. The FE head model was positioned so that the initial impact location corresponded to the maximum degree of swelling of the scalp seen in the medical images (Fig. 1). The initial velocities were taken from the MADYMO reconstruction of these accidents (Verschueren, 2009). The impacted ground was modeled as a rigid surface. The friction coefficient between the ground and the scalp was set to 0.5. All simulations were run using the LS DYNA software (version 971 revision 5.1.1 single processor). 2.1. The image correlation method The medical images of the injuries and the first principal Green Lagrange strain of the FE model were compared in Matlab (version 2013a). A summary of the process is presented in Fig. 2. The FE images that corresponded to the medical images were created in LS PrePost (version 3.2) by Section Plane for every millisecond where the brain was shown with the fringe mode for strain. To take the cumulative effect into account the images were fused together for every millisecond between 0 ms and 20 ms as illustrated at the bottom of Fig. 2. The spectrum of strain between 0.0 and 0.6 was divided into 20 fringe levels. The FE images were then resized to have the same dimensions as the medical images of the brain. The medical and FE images were transformed to binary images with the command im2bw in Matlab. The remaining white areas in the medical images corresponding to bones or small bleedings were manually removed. The cut-off strain level in the FE images was chosen so the ratio between the number of white pixels in the FE images and medical images for all sections included in the case was closest to one. Two indices were used to compare the images, Overlap Index (OI) and Location Index (LI). The OI is described by Eq. (1), where a12 is the number of white pixels shared by the medical and FE images, a1 is the total number of white pixels in the medical images and a2 is the total number of white pixels in FE images. The LI is defined as described in Eq. (2), where b1 is the distance between the center of mass (CoM) of the white regions in the medical image and the FE image and b2 is the maximum length of the section as shown in Fig. 2. A perfect match gives a value of 1 for both indices. In addition to the OI and LI, also the maximum value of the strain in the brain and the section volume was evaluated. The section volume was defined as the volume between the most superior located medical image indicating brain injury and the most inferior located medical image indicating brain injury: OI ¼
2a12 a1 þ a2
ð1Þ
LI ¼
b2 b1 b2
ð2Þ
An accident reconstruction can have several uncertainties in input data. Therefore, a sensitivity study was performed where the impact velocity and impact direction was altered. Further details can be found in Appendix B.
3. Results In Figs. 3–5 the comparison between the medical images and the strain pattern from the baseline simulations are presented. The variation of OI and LI in the sensitivity study is presented in Table 2. 4. Discussion The purpose of this study was to introduce a novel image correlation method for accident reconstructions that can be used to evaluate the results from FE simulations with medical images as
reference. Two indices were defined and used, Overlap Index (OI) and Location Index (LI). In the three accident reconstructions evaluated, OI gave low values with a maximum of 0.35 (Case 4 Section 2) compared to a perfect correlation value of 1.0. On the other hand, maximum LI showed values close to 1.0 for some section in Case 4. The OI is more sensitive than LI since OI compares on a pixel by pixel basis. Other image similarity methods could also be include, e.g. methods based on the concepts of mutual information (Viola and Wells, 1997). OI has the disadvantage of giving a large difference with a small change in the location of injury. The location of injury could be influenced by the time after the accident the medical images were taken as several studies have shown that contusions have a tendency of increasing in mass with time (Brott et al., 1997; Chang et al., 2006; Narayan et al., 2008) as well as SDH and SAH tend to disperse on the brain surface. For the three cases in this study, the medical images were taken the same day as admission to the hospital. DAI and mild TBI could be injuries more suitable for this method, since they do not have the same tendency to increase in volume over time. The prerequisite is then, of course, that the modalities such as Diffusion Tension Imaging (DTI) have a high enough resolution, as provided by the connectome scanner, to indicate areas of DAI and mild TBI. The geometry of the head could also affect the results. Like many of the head models currently in use, the model used in this study is generic, leading to differences in geometry between the medical images and the FE model. However, the size has been adjusted to fit the medical image, but some differences in shape still remains. The difference in shape between the FE model and the medical images will affect the maximum possible value of OI and LI. The difference in shape could be limited by using patientspecific models. Other factors, such as shape of the injury, will also affect the maximum possible value of OI and LI. Previous publications of FE accident reconstructions are mostly based on visual comparisons for one specific time (Anderson, 1999; Baeck, 2013; Dokko et al., 2003; Kleiven, 2007), with some exceptions. Doorly (2007) and Post et al. (2015) defined volumes in the FE model that correspond to the volumes of the injuries found in the medical images. The injured volumes were compared to the rest of the brain with respect to peak value and average values. Doorly (2007) found no correlation for peak values but some metrics showed significant higher mean value in the injured volumes. Mao and Yang (2011) estimated the contusion in controlled experiments of rats and evaluated the response with the residual error and residual percentage error for the simulations that showed spatial similarities. The suggested method in this study evaluates the results both by location and area which is a combination of the methods presented above. One difficulty with accident reconstructions is that many parameters can influence the kinematics of the victim. Bourdet et al. (2012) showed e.g. that the resultant impact velocity of the head could vary by up to 16% for two different hip riding posture just before the accidents. The initial impact velocity for the
Table 1 Descriptions of the accidents. Case Estimated bicycle velocity (km/h)
Gender Age Head injuries
4
20
Male
68
15
25
Male
65
58
6
Female 61
Hemorrhagic contusion at the right temporal lobe (burst lobe), acute subdural hematoma (ASDH) (diffuse and scattered, in addition to a small strip next to the contusion), a swelling of the scalp at the left parietal area, a small bleeding at the site of the swelling (subarachnoid blood), and a left temporal skull fracture. A fracture of the skull base and left frontal skull bone, almost midline, together with a large frontal hemorrhagic contusion and an intracranial hematoma (ICH) at the right frontal lobe due to contusion. He also suffered a subarachnoid hematoma (SAH) in the right parietal area, ASDH (right frontal basal) and DAI. A left temporal parietal skull fracture and a hemorrhagic right temporal parietal contusion, ICH at the right temporal lobe, and swelling of the scalp at the left parietal area.
Please cite this article as: Fahlstedt, M., et al., Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries. Journal of Biomechanics (2015), http://dx.doi.org/10.1016/j.jbiomech.2015.02.057i
M. Fahlstedt et al. / Journal of Biomechanics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
Fig. 1. Initial position and initial velocity for the three simulations, from left to right Case 4, Case 15 and Case 58 (v linear velocity and w angular velocity).
Fig. 2. Method for comparing the results from the FE simulations and the medical images.
Fig. 3. Comparison between FE-simulations (strain) and medical images for Case 4.
Please cite this article as: Fahlstedt, M., et al., Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries. Journal of Biomechanics (2015), http://dx.doi.org/10.1016/j.jbiomech.2015.02.057i
3
M. Fahlstedt et al. / Journal of Biomechanics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
4
Fig. 4. Comparison between FE-simulations (strain) and medical images for Case 15.
Fig. 5. Comparison between FE-simulations (strain) and medical images for Case 58.
Table 2 Variation of the average OI and LI for all sections within the Case in the sensitivity study.
Baseline Impact direction Impact velocity
OI LI OI LI OI LI
Case 4
Case 15
Case 58
0.19 0.88 0.09–0.18 0.61–0.90 0.08–0.21 0.59–0.93
0.01 0.62 0–0.01 0.61–0.66 0–0.02 0.61–0.64
0.04 0.39 0–0.13 0.25–0.90 0–0.08 0.37–0.89
accident cases in this study was determined using MADYMO simulations, while the swelling of the scalp determined the impact location. In two of the Cases (15 and 58) the fracture and swelling of the scalp were located close to one another, whereas in Case 4 the swelling was more posterior to the fracture. The swelling could be secondary to the skull fracture and not a precise indicator of the impact location. To understand the sensitivity of the impact velocity and direction a sensitivity study was performed. The baseline simulations for all three cases were close to the maximum
OI and LI. In Case 15 the smallest variations of OI and LI were found with similar range for both impact velocity and impact direction. Case 4 and Case 58 had larger range of variation than Case 15. Both these cases had similar impact locations, temporal–parietal area, whereas Case 15 had an impact to the frontal area. The brain has shown to be more sensitive to some impact directions (Gennarelli et al., 1987; Kleiven, 2006). Little is known about the friction coefficient in this type of situations. In this study a friction coefficient of 0.5 between the scalp and ground was used which is in the lower region of friction coefficients (Derler and Gerhardt, 2011). In this study a 2D comparison between the medical images showing the major injuries and the corresponding FE images was done where little attention was paid to the tissue response under and above this section volume. In none of the three cases, the maximum strain for the impact was found inside the section volume. A limitation with the method is that no difference in OI or LI is shown when the FE images have white areas compared to no white areas when at the same time the medical images have no white areas. The method could be improved by doing a 3D comparison between the medical images and FE images for the whole brain. The prerequisite for this is that medical images are digital and with a small distance between the sections. Unfortunately, the medical images were not initially available in digital format and therefore had to be scanned in this study. This study has introduced a novel image correlation method for evaluating results from FE reconstruction of real accidents and three cases with hematomas and contusions were evaluated. The method needs to be evaluated further with more cases and other types of injuries such as e.g. DAI. The proposed method may also be more suitable for controlled experiments as, for instance, in the studies presented by Mao and Yang (2011) and Anderson (1999). Performing injury reconstructions are a challenge as the information from the accidents often is uncertain. The goal is that the reconstruction can give information on how realistic the simulation results are and from that draw conclusions on injury mechanisms and preventive actions. This study has presented a method to compare how well results from a FE model of the human head correlates with injuries visible on medical images of the head from three bicycle accidents. The method of using an OI and LI is proposed to be used in future injury reconstruction studies.
Please cite this article as: Fahlstedt, M., et al., Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries. Journal of Biomechanics (2015), http://dx.doi.org/10.1016/j.jbiomech.2015.02.057i
M. Fahlstedt et al. / Journal of Biomechanics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
Conflict of interest statement The authors declare no conflict of interest. Acknowledgment This study was partly financed by Länsförsäkringarnas Cooperation Research Fund (Grant no. P1/14). The authors would also like to thank Katrien Baeck for her assistance in gathering and retrieving the information on the accident data and medical images. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.jbiomech.2015.02.057. References Anderson, R.W.G., 1999. Mechanisms of injury: an experimental and numerical study of a sheep model of head impact. In: Proceedings of the International Research Council on Biomechanics of Injury (IRCOBI) Conference. Sitges, Spain. Baeck, K., 2013. Biomechanical Modeling of Head Impacts – A Critical Analysis of Finite Element Modeling Approaches PhD thesis. KU Leuven, Leuven. Bourdet, N., Deck, C., Carreira, R.P., Willinger, R., 2012. Head impact conditions in the case of cyclist falls. Proc. Inst. Mech. Eng. P: J. Sports Eng. Technol. 226, 282–289. Brott, T., Broderick, J., Kothari, R., Barsan, W., Tomsick, T., Sauerbeck, L., Spilker, J., Duldner, J., Khoury, J., 1997. Early hemorrhage growth in patients with intracerebral hemorrhage. Stroke 28, 1–5. Chang, E., Meeker, M., Holland, M., 2006. Acute traumatic intra-parenchymal hemorrhage: risk factors for progression in the early post-injury period. Neurosurgery 58, 647–656. Coronado, V.G., Xu, L., Basavaraju, S.V., McGuire, L.C., Wald, M.M., Faul, M.D., Guzman, B.R., Hemphill, J.D., 2011. Surveillance for traumatic brain injuryrelated deaths-United States, 1997-2007. Morb. Mortal. Wkly. Rep. Surveill. Summ. 60, 1–32. Derler, S., Gerhardt, L.C., 2011. Tribology of skin: review and analysis of experimental results for the friction coefficient of human skin. Tribol. Lett. 45, 1–27. Dokko, Y., Anderson, R., Manavis, J., Blumburgs, P., McLean, J., Zhang, L., Yang, K.H., King, A.I., 2003. Validation of the human head FE model against pedestrian accident and its tentative application to the examination of the existing tolerance curve. In: Proceedings of the Enhanced Safety Vehicle (ESV) Conference. Nagoya, Japan. Doorly, M.C., 2007. Investigations into Head Injury Criteria Using Numerical Reconstruction of Real Life Accident Cases PhD thesis. University College Dublin, Dublin.
5
Fahlstedt, M., Baeck, K., Halldin, P., Vander Sloten, J., Goffin, J., Depreitere, B., Kleiven, S., 2012. Influence of impact velocity and angle in a detailed reconstruction of a bicycle accident. In: Proceedings of the International Research Council on Biomechanics of Injury (IRCOBI) Conference. Dublin, Ireland. Gennarelli, T.A., Thibault, L.E., Tomei, G., Wiser, R., Raham, D.I., Adams, J., 1987. Directional dependence of axonal brain injury due to centroidal and noncentroidal acceleration. In: Proceedings of the 31st Stapp Car Crash Conference. New Orleans, US. Hardy, W.N., Foster, C.D., Mason, M.J., Yang, K.H., King, A., Tashman, S., 2001. Investigation of head injury mechanisms using neutral density technology and high-speed biplanar X-ray. Stapp Car Crash J. 45, 1–32. Kleiven, S., 2006. Evaluation of head injury criteria using a finite element model validated against experiments on localized brain motion, intracerebral acceleration, and intracranial pressure. Int. J. Crashworthiness 11, 65–79. Kleiven, S., 2007. Predictors for traumatic brain injuries evaluated through accident reconstructions. Stapp Car Crash J. 51, 81–114. Mao, H., Yang, K.H., 2011. Investigation of brain contusion mechanism and threshold by combining finite element analysis with in vivo histology data. Int. J. Numer. Methods Biomed. Eng. 27, 357–366. Mao, H., Zhang, L., Jiang, B., Genthikatti, V.V., Jin, X., Zhu, F., Makwana, R., Gill, A., Jandir, G., Singh, A., Yang, K.H., 2013. Development of a finite element human head model partially validated with thirty five experimental cases. J. Biomech. Eng. 135, 1–15. McAllister, T.W., Ford, J.C., Ji, S., Beckwith, J.G., Flashman, L.A., Paulsen, K., Greenwald, R.M., 2012. Maximum principal strain and strain rate associated with concussion diagnosis correlates with changes in corpus callosum white matter indices. Ann. Biomed. Eng. 40, 127–140. Nahum, A.M., Smith, R., Ward, C.C., 1977. Intracranial pressure dynamics during head impact. In: Proceedings of the 21st Stapp Car Crash Conference. New Orleans, US. Narayan, R.K., Maas, A.I.R., Servadei, F., Skolnick, B.E., Tillinger, M.N., Marshall, L.F., 2008. Progression of traumatic intracerebral hemorrhage: a prospective observational study. J. Neurotrauma 25, 629–639. Post, A., Hoshizaki, T.B., Gilchrist, M.D., Brien, S., Cusimano, M., Marshall, S., 2015. Traumatic brain injuries: the influence of the direction of impact. Neurosurgery 78, 81–91. Takhounts, E.G., Ridella, S.a., Hasija, V., Tannous, R.E., Campbell, J.Q., Malone, D., Danelson, K., Stitzel, J., Rowson, S., Duma, S., 2008. Investigation of traumatic brain injuries using the next generation of simulated injury monitor (SIMon) finite element head model. Stapp Car Crash J. 52, 1–31. Trosseille, X., Tarriére, C., Lavaste, F., Guillon, F., Domont, A., 1992. Development of a F.E.M of the human head according to a specific test protocol. In: Proceedings of the 36th Stapp Car Crash Conference. Seattle, US. Verschueren, P., 2009. Biomechanical Analysis of Head Injuries Related to Bicycle Accidents and a New Bicycle Helmet Concept PhD thesis. KU Leuven, Leuven. Willinger, R., Baumgartner, D., 2003. Human head tolerance limits to specific injury mechanisms. Int. J. Crashworthiness 8, 605–617. Viola, P., Wells III, W.M., 1997. Alignment by maximization of mutual information. Int. J. Comput. Vis. 24, 137–154. Zhang, L., Yang, K.H., King, A.I., 2004. A proposed injury threshold for mild traumatic brain injury. J. Biomech. Eng. 126, 226–236.
Please cite this article as: Fahlstedt, M., et al., Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries. Journal of Biomechanics (2015), http://dx.doi.org/10.1016/j.jbiomech.2015.02.057i
Contents lists available at ScienceDirect
Journal of Biomechanics journal homepage: www.elsevier.com/locate/jbiomech www.JBiomech.com
Short communication
Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries Madelen Fahlstedt a,n, Bart Depreitere b, Peter Halldin a, Jos Vander Sloten c, Svein Kleiven a a
Neuronic Engineering, School of Technology and Health, KTH Royal Institute of Technology, Alfred Nobels Allé 10, 141 52 Huddinge, Sweden Experimental Neurosurgery and Neuroanatomy, KU Leuven, Belgium c Biomechanics, KU Leuven, Belgium b
art ic l e i nf o Article history: Accepted 28 February 2015 Keywords: Head injuries Accident reconstructions Image correlation Bicycle accidents FEA
Abstract: At present, Finite Element (FE) analyses are often used as a tool to better understand the mechanisms of head injury. Previously, these models have been compared to cadaver experiments, with the next step under development being accident reconstructions. Thus far, the main focus has been on deriving an injury threshold and little effort has been put into correlating the documented injury location with the response displayed by the FE model. Therefore, the purpose of this study was to introduce a novel image correlation method that compares the response of the FE model with medical images. The injuries shown on the medical images were compared to the strain pattern in the FE model and evaluated by two indices; the Overlap Index (OI) and the Location Index (LI). As the name suggests, OI measures the area which indicates both injury in the medical images and high strain values in the FE images. LI evaluates the difference in center of mass in the medical and FE images. A perfect match would give an OI and LI equal to 1. This method was applied to three bicycle accident reconstructions. The reconstructions gave an average OI between 0.01 and 0.19 for the three cases and between 0.39 and 0.88 for LI. Performing injury reconstructions are a challenge as the information from the accidents often is uncertain. The suggested method evaluates the response in an objective way which can be used in future injury reconstruction studies. & 2015 Elsevier Ltd. All rights reserved.
1. Introduction Traumatic Brain Injuries (TBI) result in approximately 53,000 fatalities annually in the US (Coronado et al., 2011). Fall accidents, traffic accidents and firearms are the main causes of fatal TBI (Coronado et al., 2011). To reduce the number of TBI, more knowledge is needed to understand the mechanism of injury. During the last decades, Finite Element (FE) head models have become a more common tool for studying the mechanism of head injuries (Doorly, 2007; Kleiven, 2006; Mao et al., 2013; McAllister et al., 2012; Takhounts et al., 2008; Willinger and Baumgartner, 2003; Zhang et al., 2004). Most models are compared against cadaver experiments involving intra-cranial pressure (Nahum et al., 1977), intra-cerebral acceleration (Trosseille et al., 1992) and relative motions (Hardy et al., 2001). The next step has been to compare the models against real accidents to determine thresholds for injury and non-injury situations. Until now, the accident reconstructions have mainly been focused on deriving mean values or probability values for injury metrics and
thresholds especially for concussion, while few studies have attempted to correlate injury location. Some studies have tried to visually compare the medical images and FE simulations (Anderson, 1999; Dokko et al., 2003; Fahlstedt et al., 2012; Kleiven, 2007; Mao and Yang, 2011) but few studies have tried to quantify the correlation of injury location and volume (Doorly, 2007; Mao and Yang, 2011; Post et al., 2015). The use of accident reconstructions to evaluate FE models and, in turn, using them to better understand the mechanism of the head injury is becoming increasingly prevalent. To be able to fully use the models in accident reconstruction not only the threshold for injury needs to be evaluated but also the location of injury. No objective, statistics-based image correlation method has previously been used to correlate injury patterns from real-life TBI with output from FE analysis. Therefore, the purpose of this study was to introduce a novel image correlation method that compares the response of the FE model with medical images and applies this to FE bicycle accident reconstructions.
2. Methods n
Corresponding author. Tel.: þ 46 87904876; fax: þ 46 8 21 83 68. E-mail address: [email protected] (M. Fahlstedt).
In this study the novel image correlation method between FE simulations and medical images was evaluated on three single bicycle accident cases collected by
http://dx.doi.org/10.1016/j.jbiomech.2015.02.057 0021-9290/& 2015 Elsevier Ltd. All rights reserved.
Please cite this article as: Fahlstedt, M., et al., Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries. Journal of Biomechanics (2015), http://dx.doi.org/10.1016/j.jbiomech.2015.02.057i
M. Fahlstedt et al. / Journal of Biomechanics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
2
KU Leuven (Table 1). The accident reconstructions of the head injuries were performed with the KTH Royal Institute of Technology head model (Kleiven, 2007), slightly modified as described in Appendix A. The FE head model was positioned so that the initial impact location corresponded to the maximum degree of swelling of the scalp seen in the medical images (Fig. 1). The initial velocities were taken from the MADYMO reconstruction of these accidents (Verschueren, 2009). The impacted ground was modeled as a rigid surface. The friction coefficient between the ground and the scalp was set to 0.5. All simulations were run using the LS DYNA software (version 971 revision 5.1.1 single processor). 2.1. The image correlation method The medical images of the injuries and the first principal Green Lagrange strain of the FE model were compared in Matlab (version 2013a). A summary of the process is presented in Fig. 2. The FE images that corresponded to the medical images were created in LS PrePost (version 3.2) by Section Plane for every millisecond where the brain was shown with the fringe mode for strain. To take the cumulative effect into account the images were fused together for every millisecond between 0 ms and 20 ms as illustrated at the bottom of Fig. 2. The spectrum of strain between 0.0 and 0.6 was divided into 20 fringe levels. The FE images were then resized to have the same dimensions as the medical images of the brain. The medical and FE images were transformed to binary images with the command im2bw in Matlab. The remaining white areas in the medical images corresponding to bones or small bleedings were manually removed. The cut-off strain level in the FE images was chosen so the ratio between the number of white pixels in the FE images and medical images for all sections included in the case was closest to one. Two indices were used to compare the images, Overlap Index (OI) and Location Index (LI). The OI is described by Eq. (1), where a12 is the number of white pixels shared by the medical and FE images, a1 is the total number of white pixels in the medical images and a2 is the total number of white pixels in FE images. The LI is defined as described in Eq. (2), where b1 is the distance between the center of mass (CoM) of the white regions in the medical image and the FE image and b2 is the maximum length of the section as shown in Fig. 2. A perfect match gives a value of 1 for both indices. In addition to the OI and LI, also the maximum value of the strain in the brain and the section volume was evaluated. The section volume was defined as the volume between the most superior located medical image indicating brain injury and the most inferior located medical image indicating brain injury: OI ¼
2a12 a1 þ a2
ð1Þ
LI ¼
b2 b1 b2
ð2Þ
An accident reconstruction can have several uncertainties in input data. Therefore, a sensitivity study was performed where the impact velocity and impact direction was altered. Further details can be found in Appendix B.
3. Results In Figs. 3–5 the comparison between the medical images and the strain pattern from the baseline simulations are presented. The variation of OI and LI in the sensitivity study is presented in Table 2. 4. Discussion The purpose of this study was to introduce a novel image correlation method for accident reconstructions that can be used to evaluate the results from FE simulations with medical images as
reference. Two indices were defined and used, Overlap Index (OI) and Location Index (LI). In the three accident reconstructions evaluated, OI gave low values with a maximum of 0.35 (Case 4 Section 2) compared to a perfect correlation value of 1.0. On the other hand, maximum LI showed values close to 1.0 for some section in Case 4. The OI is more sensitive than LI since OI compares on a pixel by pixel basis. Other image similarity methods could also be include, e.g. methods based on the concepts of mutual information (Viola and Wells, 1997). OI has the disadvantage of giving a large difference with a small change in the location of injury. The location of injury could be influenced by the time after the accident the medical images were taken as several studies have shown that contusions have a tendency of increasing in mass with time (Brott et al., 1997; Chang et al., 2006; Narayan et al., 2008) as well as SDH and SAH tend to disperse on the brain surface. For the three cases in this study, the medical images were taken the same day as admission to the hospital. DAI and mild TBI could be injuries more suitable for this method, since they do not have the same tendency to increase in volume over time. The prerequisite is then, of course, that the modalities such as Diffusion Tension Imaging (DTI) have a high enough resolution, as provided by the connectome scanner, to indicate areas of DAI and mild TBI. The geometry of the head could also affect the results. Like many of the head models currently in use, the model used in this study is generic, leading to differences in geometry between the medical images and the FE model. However, the size has been adjusted to fit the medical image, but some differences in shape still remains. The difference in shape between the FE model and the medical images will affect the maximum possible value of OI and LI. The difference in shape could be limited by using patientspecific models. Other factors, such as shape of the injury, will also affect the maximum possible value of OI and LI. Previous publications of FE accident reconstructions are mostly based on visual comparisons for one specific time (Anderson, 1999; Baeck, 2013; Dokko et al., 2003; Kleiven, 2007), with some exceptions. Doorly (2007) and Post et al. (2015) defined volumes in the FE model that correspond to the volumes of the injuries found in the medical images. The injured volumes were compared to the rest of the brain with respect to peak value and average values. Doorly (2007) found no correlation for peak values but some metrics showed significant higher mean value in the injured volumes. Mao and Yang (2011) estimated the contusion in controlled experiments of rats and evaluated the response with the residual error and residual percentage error for the simulations that showed spatial similarities. The suggested method in this study evaluates the results both by location and area which is a combination of the methods presented above. One difficulty with accident reconstructions is that many parameters can influence the kinematics of the victim. Bourdet et al. (2012) showed e.g. that the resultant impact velocity of the head could vary by up to 16% for two different hip riding posture just before the accidents. The initial impact velocity for the
Table 1 Descriptions of the accidents. Case Estimated bicycle velocity (km/h)
Gender Age Head injuries
4
20
Male
68
15
25
Male
65
58
6
Female 61
Hemorrhagic contusion at the right temporal lobe (burst lobe), acute subdural hematoma (ASDH) (diffuse and scattered, in addition to a small strip next to the contusion), a swelling of the scalp at the left parietal area, a small bleeding at the site of the swelling (subarachnoid blood), and a left temporal skull fracture. A fracture of the skull base and left frontal skull bone, almost midline, together with a large frontal hemorrhagic contusion and an intracranial hematoma (ICH) at the right frontal lobe due to contusion. He also suffered a subarachnoid hematoma (SAH) in the right parietal area, ASDH (right frontal basal) and DAI. A left temporal parietal skull fracture and a hemorrhagic right temporal parietal contusion, ICH at the right temporal lobe, and swelling of the scalp at the left parietal area.
Please cite this article as: Fahlstedt, M., et al., Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries. Journal of Biomechanics (2015), http://dx.doi.org/10.1016/j.jbiomech.2015.02.057i
M. Fahlstedt et al. / Journal of Biomechanics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
Fig. 1. Initial position and initial velocity for the three simulations, from left to right Case 4, Case 15 and Case 58 (v linear velocity and w angular velocity).
Fig. 2. Method for comparing the results from the FE simulations and the medical images.
Fig. 3. Comparison between FE-simulations (strain) and medical images for Case 4.
Please cite this article as: Fahlstedt, M., et al., Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries. Journal of Biomechanics (2015), http://dx.doi.org/10.1016/j.jbiomech.2015.02.057i
3
M. Fahlstedt et al. / Journal of Biomechanics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
4
Fig. 4. Comparison between FE-simulations (strain) and medical images for Case 15.
Fig. 5. Comparison between FE-simulations (strain) and medical images for Case 58.
Table 2 Variation of the average OI and LI for all sections within the Case in the sensitivity study.
Baseline Impact direction Impact velocity
OI LI OI LI OI LI
Case 4
Case 15
Case 58
0.19 0.88 0.09–0.18 0.61–0.90 0.08–0.21 0.59–0.93
0.01 0.62 0–0.01 0.61–0.66 0–0.02 0.61–0.64
0.04 0.39 0–0.13 0.25–0.90 0–0.08 0.37–0.89
accident cases in this study was determined using MADYMO simulations, while the swelling of the scalp determined the impact location. In two of the Cases (15 and 58) the fracture and swelling of the scalp were located close to one another, whereas in Case 4 the swelling was more posterior to the fracture. The swelling could be secondary to the skull fracture and not a precise indicator of the impact location. To understand the sensitivity of the impact velocity and direction a sensitivity study was performed. The baseline simulations for all three cases were close to the maximum
OI and LI. In Case 15 the smallest variations of OI and LI were found with similar range for both impact velocity and impact direction. Case 4 and Case 58 had larger range of variation than Case 15. Both these cases had similar impact locations, temporal–parietal area, whereas Case 15 had an impact to the frontal area. The brain has shown to be more sensitive to some impact directions (Gennarelli et al., 1987; Kleiven, 2006). Little is known about the friction coefficient in this type of situations. In this study a friction coefficient of 0.5 between the scalp and ground was used which is in the lower region of friction coefficients (Derler and Gerhardt, 2011). In this study a 2D comparison between the medical images showing the major injuries and the corresponding FE images was done where little attention was paid to the tissue response under and above this section volume. In none of the three cases, the maximum strain for the impact was found inside the section volume. A limitation with the method is that no difference in OI or LI is shown when the FE images have white areas compared to no white areas when at the same time the medical images have no white areas. The method could be improved by doing a 3D comparison between the medical images and FE images for the whole brain. The prerequisite for this is that medical images are digital and with a small distance between the sections. Unfortunately, the medical images were not initially available in digital format and therefore had to be scanned in this study. This study has introduced a novel image correlation method for evaluating results from FE reconstruction of real accidents and three cases with hematomas and contusions were evaluated. The method needs to be evaluated further with more cases and other types of injuries such as e.g. DAI. The proposed method may also be more suitable for controlled experiments as, for instance, in the studies presented by Mao and Yang (2011) and Anderson (1999). Performing injury reconstructions are a challenge as the information from the accidents often is uncertain. The goal is that the reconstruction can give information on how realistic the simulation results are and from that draw conclusions on injury mechanisms and preventive actions. This study has presented a method to compare how well results from a FE model of the human head correlates with injuries visible on medical images of the head from three bicycle accidents. The method of using an OI and LI is proposed to be used in future injury reconstruction studies.
Please cite this article as: Fahlstedt, M., et al., Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries. Journal of Biomechanics (2015), http://dx.doi.org/10.1016/j.jbiomech.2015.02.057i
M. Fahlstedt et al. / Journal of Biomechanics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
Conflict of interest statement The authors declare no conflict of interest. Acknowledgment This study was partly financed by Länsförsäkringarnas Cooperation Research Fund (Grant no. P1/14). The authors would also like to thank Katrien Baeck for her assistance in gathering and retrieving the information on the accident data and medical images. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.jbiomech.2015.02.057. References Anderson, R.W.G., 1999. Mechanisms of injury: an experimental and numerical study of a sheep model of head impact. In: Proceedings of the International Research Council on Biomechanics of Injury (IRCOBI) Conference. Sitges, Spain. Baeck, K., 2013. Biomechanical Modeling of Head Impacts – A Critical Analysis of Finite Element Modeling Approaches PhD thesis. KU Leuven, Leuven. Bourdet, N., Deck, C., Carreira, R.P., Willinger, R., 2012. Head impact conditions in the case of cyclist falls. Proc. Inst. Mech. Eng. P: J. Sports Eng. Technol. 226, 282–289. Brott, T., Broderick, J., Kothari, R., Barsan, W., Tomsick, T., Sauerbeck, L., Spilker, J., Duldner, J., Khoury, J., 1997. Early hemorrhage growth in patients with intracerebral hemorrhage. Stroke 28, 1–5. Chang, E., Meeker, M., Holland, M., 2006. Acute traumatic intra-parenchymal hemorrhage: risk factors for progression in the early post-injury period. Neurosurgery 58, 647–656. Coronado, V.G., Xu, L., Basavaraju, S.V., McGuire, L.C., Wald, M.M., Faul, M.D., Guzman, B.R., Hemphill, J.D., 2011. Surveillance for traumatic brain injuryrelated deaths-United States, 1997-2007. Morb. Mortal. Wkly. Rep. Surveill. Summ. 60, 1–32. Derler, S., Gerhardt, L.C., 2011. Tribology of skin: review and analysis of experimental results for the friction coefficient of human skin. Tribol. Lett. 45, 1–27. Dokko, Y., Anderson, R., Manavis, J., Blumburgs, P., McLean, J., Zhang, L., Yang, K.H., King, A.I., 2003. Validation of the human head FE model against pedestrian accident and its tentative application to the examination of the existing tolerance curve. In: Proceedings of the Enhanced Safety Vehicle (ESV) Conference. Nagoya, Japan. Doorly, M.C., 2007. Investigations into Head Injury Criteria Using Numerical Reconstruction of Real Life Accident Cases PhD thesis. University College Dublin, Dublin.
5
Fahlstedt, M., Baeck, K., Halldin, P., Vander Sloten, J., Goffin, J., Depreitere, B., Kleiven, S., 2012. Influence of impact velocity and angle in a detailed reconstruction of a bicycle accident. In: Proceedings of the International Research Council on Biomechanics of Injury (IRCOBI) Conference. Dublin, Ireland. Gennarelli, T.A., Thibault, L.E., Tomei, G., Wiser, R., Raham, D.I., Adams, J., 1987. Directional dependence of axonal brain injury due to centroidal and noncentroidal acceleration. In: Proceedings of the 31st Stapp Car Crash Conference. New Orleans, US. Hardy, W.N., Foster, C.D., Mason, M.J., Yang, K.H., King, A., Tashman, S., 2001. Investigation of head injury mechanisms using neutral density technology and high-speed biplanar X-ray. Stapp Car Crash J. 45, 1–32. Kleiven, S., 2006. Evaluation of head injury criteria using a finite element model validated against experiments on localized brain motion, intracerebral acceleration, and intracranial pressure. Int. J. Crashworthiness 11, 65–79. Kleiven, S., 2007. Predictors for traumatic brain injuries evaluated through accident reconstructions. Stapp Car Crash J. 51, 81–114. Mao, H., Yang, K.H., 2011. Investigation of brain contusion mechanism and threshold by combining finite element analysis with in vivo histology data. Int. J. Numer. Methods Biomed. Eng. 27, 357–366. Mao, H., Zhang, L., Jiang, B., Genthikatti, V.V., Jin, X., Zhu, F., Makwana, R., Gill, A., Jandir, G., Singh, A., Yang, K.H., 2013. Development of a finite element human head model partially validated with thirty five experimental cases. J. Biomech. Eng. 135, 1–15. McAllister, T.W., Ford, J.C., Ji, S., Beckwith, J.G., Flashman, L.A., Paulsen, K., Greenwald, R.M., 2012. Maximum principal strain and strain rate associated with concussion diagnosis correlates with changes in corpus callosum white matter indices. Ann. Biomed. Eng. 40, 127–140. Nahum, A.M., Smith, R., Ward, C.C., 1977. Intracranial pressure dynamics during head impact. In: Proceedings of the 21st Stapp Car Crash Conference. New Orleans, US. Narayan, R.K., Maas, A.I.R., Servadei, F., Skolnick, B.E., Tillinger, M.N., Marshall, L.F., 2008. Progression of traumatic intracerebral hemorrhage: a prospective observational study. J. Neurotrauma 25, 629–639. Post, A., Hoshizaki, T.B., Gilchrist, M.D., Brien, S., Cusimano, M., Marshall, S., 2015. Traumatic brain injuries: the influence of the direction of impact. Neurosurgery 78, 81–91. Takhounts, E.G., Ridella, S.a., Hasija, V., Tannous, R.E., Campbell, J.Q., Malone, D., Danelson, K., Stitzel, J., Rowson, S., Duma, S., 2008. Investigation of traumatic brain injuries using the next generation of simulated injury monitor (SIMon) finite element head model. Stapp Car Crash J. 52, 1–31. Trosseille, X., Tarriére, C., Lavaste, F., Guillon, F., Domont, A., 1992. Development of a F.E.M of the human head according to a specific test protocol. In: Proceedings of the 36th Stapp Car Crash Conference. Seattle, US. Verschueren, P., 2009. Biomechanical Analysis of Head Injuries Related to Bicycle Accidents and a New Bicycle Helmet Concept PhD thesis. KU Leuven, Leuven. Willinger, R., Baumgartner, D., 2003. Human head tolerance limits to specific injury mechanisms. Int. J. Crashworthiness 8, 605–617. Viola, P., Wells III, W.M., 1997. Alignment by maximization of mutual information. Int. J. Comput. Vis. 24, 137–154. Zhang, L., Yang, K.H., King, A.I., 2004. A proposed injury threshold for mild traumatic brain injury. J. Biomech. Eng. 126, 226–236.
Please cite this article as: Fahlstedt, M., et al., Correlation between Injury Pattern and Finite Element Analysis in Biomechanical Reconstructions of Traumatic Brain Injuries. Journal of Biomechanics (2015), http://dx.doi.org/10.1016/j.jbiomech.2015.02.057i
