• 1. Bratton S, Chestnut R, Ghajar J, et al. Guidelines for the management of severe traumatic brain injury. VIII. Intracranial pressure thresholds. J Neurotrauma 2006; 24: S55S58.

    • Search Google Scholar
    • Export Citation
  • 2. Rangel-Castillo L, Gopinath S, Robertson CS. Management of intracranial hypertension. Neurol Clin 2008; 26: 521541.

  • 3. Dewey CW. Head trauma management (chapter 6). In: A practical guide to canine and feline neurology. 2nd ed. Ames, Iowa:Wiley-Blackwell. 2008; 223.

    • Search Google Scholar
    • Export Citation
  • 4. Piper I. Intracranial pressure and elastance. In: Reilly PL, Bullock R, eds. Head injury: pathophysiology and management. 2nd ed. Boca Raton, Fla: CRC Press, 2005; 93112.

    • Search Google Scholar
    • Export Citation
  • 5. Ryder HW, Espey FF, Kristoff FV, et al. Observations on the interrelationships of intracranial pressure and cerebral blood flow. J Neurosurg 1951; 8: 4658.

    • Search Google Scholar
    • Export Citation
  • 6. Popovic D, Khoo M, Lee S. Noninvasive monitoring of intracranial pressure. Recent Patents Biomed Eng 2009; 2: 165179.

  • 7. Zhong J, Dujovny M, Park HK, et al. Advances in intracranial pressure monitoring techniques. Neurol Res 2003; 25: 339350.

  • 8. Pattinson K, Wynne-Jones G, Imray CHE. Monitoring intracranial pressure, perfusion and metabolism. Contin Educ Anaesth Crit Care Pain 2005; 5: 130133.

    • Search Google Scholar
    • Export Citation
  • 9. Smith M. Monitoring intracranial pressure in traumatic brain injury. Anesth Analg 2008; 106: 240248.

  • 10. Raboel P, Bartek J, Andresen M, et al. Intracranial pressure monitoring: invasive versus non-invasive methods—a review. Crit Care Res Pract [serial online]. 2012; 2012: 950393. Available at: www.hindawi.com/journals/ccrp/2012/950393/. Accessed Feb 28, 2014.

    • Search Google Scholar
    • Export Citation
  • 11. Marmarou A, Shulman K, LaMorgese J. Compartmental analysis of compliance and outflow resistance of the cerebrospinal fluid system. J Neurosurg 1975; 43: 523534.

    • Search Google Scholar
    • Export Citation
  • 12. Marmarou A, Shulman K, Rosende RM. A nonlinear analysis of the cerebrospinal fluid system and intracranial pressure dynamics. J Neurosurg 1978; 48: 332344.

    • Search Google Scholar
    • Export Citation
  • 13. Sivaloganathan S, Tenti G, Drake J. Mathematical pressure volume models of the cerebrospinal fluid. Appl Math Comput 1998; 94: 243266.

    • Search Google Scholar
    • Export Citation
  • 14. Miller J, Garibi J. Intracranial volume/pressure relationships during continuous monitoring of ventricular fluid pressure. In: Brock M, Dietz H, eds. Intracranial pressure. Berlin: Springer-Verlag, 1972; 270274.

    • Search Google Scholar
    • Export Citation
  • 15. Miller JD, Garibi J, Pickard JD. Induced changes of cerebrospinal fluid volume: effects during continuous monitoring of ventricular fluid pressure. Arch Neurol 1973; 28: 265269.

    • Search Google Scholar
    • Export Citation
  • 16. Kurtcuoglu V, Poulikakos D, Ventikos Y. Computational modeling of the mechanical behavior of the cerebrospinal fluid system. J Biomech Eng 2005; 127: 264269.

    • Search Google Scholar
    • Export Citation
  • 17. Wittek A, Miller K, Kikinis R, et al. Patient-specific model of brain deformation: application to medical image registration. J Biomech 2007; 40: 919929.

    • Search Google Scholar
    • Export Citation
  • 18. Dutta-Roy T, Wittek A, Miller K. Biomechanical modelling of normal pressure hydrocephalus. J Biomech 2008; 41: 22632271.

  • 19. Miller K, Wittek A, Joldes G, et al. Modelling brain deformations for computer-integrated neurosurgery. Int J Numer Method Biomed Eng 2010; 26: 117138.

    • Search Google Scholar
    • Export Citation
  • 20. Yang KH, King AI. Modeling of the brain for injury simulation and prevention. In: Miller K, ed. Biomechanics of the brain. New York: Springer, 2011; 91110.

    • Search Google Scholar
    • Export Citation
  • 21. Yang KH, Mao H, Wagner C, et al. Modeling of the brain for injury prevention. In: Bilston LE, ed. Neural tissue biomechanics. Berlin: Springer-Verlag, 2011; 69120.

    • Search Google Scholar
    • Export Citation
  • 22. Haidekker M. Image analysis and visualization software (chapter 14). In: Advanced biomedical image analysis. Hoboken, NJ: John Wiley & Sons, 2011; 456461.

    • Search Google Scholar
    • Export Citation
  • 23. Bronson JR, Levine JA, Whitaker RT. Lattice cleaving: conforming tetrahedral meshes of multimaterial domains with bounded quality, in Proceedings. 21st Int Meshing Roundtable 2013; 191209.

    • Search Google Scholar
    • Export Citation
  • 24. Changaris DG, McGraw CP, Richardson JD, et al. Correlation of cerebral perfusion pressure and Glasgow Coma Scale to outcome. J Trauma 1987; 27: 10071013.

    • Search Google Scholar
    • Export Citation
  • 25. McGraw C. A cerebral perfusion pressure greater than 80 mm Hg is more beneficial. In: Hoff JT, Betz AL, eds. Intracranial pressure VII. Berlin: Springer-Verlag, 1989; 839841.

    • Search Google Scholar
    • Export Citation
  • 26. Lin S, Shieh S, Grimm M. Ultrasonic measurements of brain tissue properties, in Proceedings. Symp Centers Dis Control Prevent 1997; 2731.

    • Search Google Scholar
    • Export Citation
  • 27. Yang J. Investigation of brain trauma biomechanics in vehicle traffic accidents using human body computational models. In: Wittek A, Nielsen PMF, Miller K, eds. Computational biomechanics for medicine. New York: Springer, 2011; 514.

    • Search Google Scholar
    • Export Citation
  • 28. Omori K, Zhang L, Yang KH, et al. Effect of cerebral vasculatures on the mechanical response of brain tissue: a preliminary study, in Proceedings. ASME Int Mech Eng Cong Exposition 2000; 246: 167174.

    • Search Google Scholar
    • Export Citation
  • 29. Kaczmarek M, Subramaniam RP, Neff SR. The hydromechanics of hydrocephalus: steady-state solutions for cylindrical geometry. Bull Math Biol 1997; 59: 295323.

    • Search Google Scholar
    • Export Citation
  • 30. Miller K. Constitutive model of brain tissue suitable for finite element analysis of surgical procedures. J Biomech 1999; 32: 531537.

    • Search Google Scholar
    • Export Citation
  • 31. Dassios G, Kiriakopoulos M, Kostopoulos V. On the sensitivity of the vibrational response of the human head. Comput Mech 1998; 21: 382388.

    • Search Google Scholar
    • Export Citation
  • 32. Nagashima T, Tamaki N, Matsumoto S, et al. Biomechanics of hydrocephalus: a new theoretical model. Neurosurgery 1987; 21: 898904.

  • 33. Tada Y, Matsumoto R, Nishimura Y. Mechanical modelings of the brain and simulation of the biomechanism of hydrocephalus. Trans Jpn Soc Mech Eng 1990; 33: 269275.

    • Search Google Scholar
    • Export Citation
  • 34. Jacobson EE, Fletcher DF, Morgan MK, et al. Fluid dynamics of the cerebral aqueduct. Pediatr Neurosurg 1996; 24: 229236.

  • 35. Peña A, Bolton MD, Whitehouse H, et al. Effects of brain ventricular shape on periventricular biomechanics: a finite-element analysis. Neurosurgery 1999; 45: 107116.

    • Search Google Scholar
    • Export Citation
  • 36. Taylor Z, Miller K. Reassessment of brain elasticity for analysis of biomechanisms of hydrocephalus. J Biomech 2004; 37: 12631269.

    • Search Google Scholar
    • Export Citation
  • 37. Linninger AA, Xenos M, Zhu DC, et al. Cerebrospinal fluid flow in the normal and hydrocephalic human brain. IEEE Trans Biomed Eng 2007; 54: 291302.

    • Search Google Scholar
    • Export Citation
  • 38. Gilchrist M, O'Donoghue D. Simulation of the development of frontal head impact injury. Comput Mech 2000; 26: 229235.

  • 39. Gong S, Lee H, Lu C. Computational simulation of the human head response to non-contact impact. Comput Struc 2008; 86: 758770.

  • 40. Li Z, Luo Y. Finite element study of correlation between intracranial pressure and external vibration responses of human head. Adv Theor Appl Mech 2010; 3: 139149.

    • Search Google Scholar
    • Export Citation
  • 41. Valencia A, Blas B, Ortega JH. Modeling of brain shift phenomenon for different craniotomies and solid models. J Appl Math [serial online]. 2012; 2012: 409127. Available at: www.hindawi.com/journals/jam/2012/409127/. Accessed Feb 28, 2014.

    • Search Google Scholar
    • Export Citation
  • 42. Yue X, Wang L, Wang R. Tissue modeling and analyzing with finite element method: a review for cranium brain imaging. Int J Biomed Imaging [serial online]. 2013; 2013: 781603. Available at: www.hindawi.com/journals/ijbi/2013/781603/. Accessed Feb 28, 2014.

    • Search Google Scholar
    • Export Citation
  • 43. Levine DN. Intracranial pressure and ventricular expansion in hydrocephalus: have we been asking the wrong question? J Neurol Sci 2008; 269: 111.

    • Search Google Scholar
    • Export Citation
  • 44. Levine DN. The pathogenesis of normal pressure hydrocephalus: a theoretical analysis. Bull Math Biol 1999; 61: 875916.

  • 45. Kyriacou SK, Davatzikos C, Zinreich SJ, et al. Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model. IEEE Trans Med Imaging 1999; 18: 580592.

    • Search Google Scholar
    • Export Citation

Advertisement

Noninvasive assessment of intracranial pressure in dogs by use of biomechanical response behavior, diagnostic imaging, and finite element analysis

View More View Less
  • 1 College of Engineering, University of Georgia, Athens, GA 30602.
  • | 2 Department of Veterinary Bioscience and Diagnostic Imaging, College of Veterinary Medicine, University of Georgia, Athens, GA 30602.
  • | 3 College of Engineering, University of Georgia, Athens, GA 30602.

Abstract

OBJECTIVE To develop a novel method for use of diagnostic imaging, finite element analysis (FEA), and simulated biomechanical response behavior of brain tissue in noninvasive assessment and estimation of intracranial pressure (ICP) of dogs.

SAMPLE MRI data for 5 dogs.

PROCEDURES MRI data for 5 dogs (1 with a geometrically normal brain that had no detectable signs of injury or disease and 4 with various degrees of geometric abnormalities) were obtained from a digital imaging archiving and communication system database. Patient-specific 3-D models composed of exact brain geometries were constructed from MRI images. Finite element analysis was used to simulate and observe patterns of nonlinear biphasic biomechanical response behavior of geometrically normal and abnormal canine brains at various levels of decreasing cerebral perfusion pressure and increasing ICP.

RESULTS Changes in biomechanical response behavior were detected with FEA for decreasing cerebral perfusion pressure and increasing ICP. Abnormalities in brain geometry led to observable changes in deformation and biomechanical response behavior for increased ICP, compared with results for geometrically normal brains.

CONCLUSIONS AND CLINICAL RELEVANCE In this study, patient-specific critical ICP was identified, which could be useful as a method to predict the onset of brain herniation. Results indicated that it was feasible to apply FEA to brain geometry obtained from MRI data of clinical patients and to use biomechanical response behavior resulting from increased ICP as a diagnostic and prognostic method to noninvasively assess or classify levels of brain injury in clinical veterinary settings.

Abstract

OBJECTIVE To develop a novel method for use of diagnostic imaging, finite element analysis (FEA), and simulated biomechanical response behavior of brain tissue in noninvasive assessment and estimation of intracranial pressure (ICP) of dogs.

SAMPLE MRI data for 5 dogs.

PROCEDURES MRI data for 5 dogs (1 with a geometrically normal brain that had no detectable signs of injury or disease and 4 with various degrees of geometric abnormalities) were obtained from a digital imaging archiving and communication system database. Patient-specific 3-D models composed of exact brain geometries were constructed from MRI images. Finite element analysis was used to simulate and observe patterns of nonlinear biphasic biomechanical response behavior of geometrically normal and abnormal canine brains at various levels of decreasing cerebral perfusion pressure and increasing ICP.

RESULTS Changes in biomechanical response behavior were detected with FEA for decreasing cerebral perfusion pressure and increasing ICP. Abnormalities in brain geometry led to observable changes in deformation and biomechanical response behavior for increased ICP, compared with results for geometrically normal brains.

CONCLUSIONS AND CLINICAL RELEVANCE In this study, patient-specific critical ICP was identified, which could be useful as a method to predict the onset of brain herniation. Results indicated that it was feasible to apply FEA to brain geometry obtained from MRI data of clinical patients and to use biomechanical response behavior resulting from increased ICP as a diagnostic and prognostic method to noninvasively assess or classify levels of brain injury in clinical veterinary settings.

Contributor Notes

Dr. Madison's present address is 862 Park Ave, Fairfield, AL 35064.

Address correspondence to Dr. Sharma (as7930@uga.edu).