MRI-based anatomical model of the human head for specific absorption rate mapping | Medical & Biological Engineering & Computing Skip to main content

Advertisement

Springer Nature Link
Log in

MRI-based anatomical model of the human head for specific absorption rate mapping

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

In this study, we present a magnetic resonance imaging (MRI)-based, high-resolution, numerical model of the head of a healthy human subject. In order to formulate the model, we performed quantitative volumetric segmentation on the human head, using T1-weighted MRI. The high spatial resolution used (1 × 1 × 1 mm3), allowed for the precise computation and visualization of a higher number of anatomical structures than provided by previous models. Furthermore, the high spatial resolution allowed us to study individual thin anatomical structures of clinical relevance not visible by the standard model currently adopted in computational bioelectromagnetics. When we computed the electromagnetic field and specific absorption rate (SAR) at 7 Tesla MRI using this high-resolution model, we were able to obtain a detailed visualization of such fine anatomical structures as the epidermis/dermis, bone structures, bone-marrow, white matter and nasal and eye structures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Kainz W et al (2005) Dosimetric comparison of the specific anthropomorphic mannequin (SAM) to 14 anatomical head models using a novel definition for the mobile phone positioning. Phys Med Biol 50(14):3423–3445. doi:10.1088/0031-9155/50/14/016

    Article  Google Scholar 

  2. Gandhi OP, Chen XB (1999) Specific absorption rates and induced current densities for an anatomy-based model of the human for exposure to time-varying magnetic fields of MRI. Magn Reson Med 41(4):816–823. doi :10.1002/(SICI)1522-2594(199904)41:4<816::AID-MRM22>3.0.CO;2-5

    Article  Google Scholar 

  3. Ibrahim TS et al (2005) Electromagnetic perspective on the operation of RF coils at 1.5–11.7 Tesla. Magn Reson Med 54(3):683–690. doi:10.1002/mrm.20596

    Article  Google Scholar 

  4. Collins CM, Smith MB (2001) Calculations of B(1) distribution, SNR, and SAR for a surface coil adjacent to an anatomically-accurate human body model. Magn Reson Med 45(4):692–699. doi:10.1002/mrm.1092

    Article  Google Scholar 

  5. Christ A et al (2006) Characterization of the electromagnetic near-field absorption in layered biological tissue in the frequency range from 30 MHz to 6, 000 MHz. Phys Med Biol 51(19):4951–4965. doi:10.1088/0031-9155/51/19/014

    Article  Google Scholar 

  6. Gabriel C, Gabriel S, Corthout E (1996) The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Phys Med Biol 41:2251–2269. doi:10.1088/0031-9155/41/11/002

    Article  Google Scholar 

  7. Ibrahim TS et al (2007) In-depth study of the electromagnetics of ultrahigh-field MRI. NMR Biomed 20(1):58–68. doi:10.1002/nbm.1094

    Article  Google Scholar 

  8. Chou CK et al (1996) Radio frequency electromagnetic exposure: tutorial review on experimental dosimetry. Bioelectromagnetics 17(3):195–208. doi :10.1002/(SICI)1521-186X(1996)17:3<195::AID-BEM5>3.0.CO;2-Z

    Article  Google Scholar 

  9. Nitz WR et al (2005) Specific absorption rate as a poor indicator of magnetic resonance-related implant heating. Invest Radiol 40(12):773–776. doi:10.1097/01.rli.0000185898.59140.91

    Article  Google Scholar 

  10. FDA (2003) Criteria for significant risk investigations of magnetic resonance diagnostic devices. Center for Devices and Radiological Health

  11. IEC (2002) International Standard, medical equipment, part 2–33: particular requirements for the safety of the magnetic resonance equipment for medical diagnosis, 2nd revision. International Electrotechnical Commission 601-2-33, Geneva, p 29–31

  12. Kunz KS, Luebbers RJ (1993) The finite difference time domain method for electromagnetics. CRC Press, Boca Raton, p 448

    Google Scholar 

  13. Taflove A, Hagness SC (2000) Computational electrodynamics: the finite-difference time-domain method. Artech House, Boston

    MATH  Google Scholar 

  14. Fischl B et al (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341–355. doi:10.1016/S0896-6273(02)00569-X

    Article  Google Scholar 

  15. Testut L, Jacob O (1974) Trattato di Anatomia Topografica con applicazioni medico-chirurgiche, ed UTET. Turin, Italy

  16. Filipek PA et al (1994) The young adult human brain: an MRI-based morphometric analysis. Cereb Cortex 4(4):344–360. doi:10.1093/cercor/4.4.344

    Article  Google Scholar 

  17. Fawcett D, Jensh R (2002) Bloom and Fawcett’s concise histology, 2nd edn. H. Arnold. London

  18. Gray H, Williams PL (2092) Bannister LH (1995) Gray’s anatomy: the anatomical basis of medicine and surgery, 38th edn. Churchill Livingstone, New York, p 2092

    Google Scholar 

  19. v Vorst AA Rosen, Kotsuka Y (2006) RF/microwave interaction with biological tissues. In: Wiley series in microwave and optical engineering. Wiley, Hoboken, xiii, p 330

  20. Gabriel C, Gabriel S, Corthout E (1996) The dielectric properties of biological tissues: I. Literature survey. Phys Med Biol 41:2231–2249. FCC, http://www.fcc.gov/fcc-bin/dielec.sh

    Google Scholar 

  21. Collins CM, Liu W, Wang J, Gruetter R, Vaughan JT, Ugurbil K, Smith MB (2004) Temperature and SAR calculations for a human head within volume and surface coils at 64 and 300 MHz. J Magn Reson Imaging 19:650–656

    Article  Google Scholar 

  22. DeMarco SC, Lazzi G, Liu W, Weiland JD, Humayun MS (2003) Computed SAR and thermal elevation in a 0.25 mm 2-D model of the human eye and head in response to an implanted retinal stimulator—part I: models and methods. Antennas and propagation. IEEE Trans 51:2274–2285

    Google Scholar 

  23. Li QX, Gandhi OP (2006) Thermal implications of the new relaxed IEEE RF safety standard for head exposures to cellular telephones at 835 and 1,900 MHz. IEEE Trans Microw Theory Tech 54:3146–3154

    Article  Google Scholar 

  24. Gabriel C, Gabriel S, Corthout E (1996) The dielectric properties of biological tissues: I. Literature survey. Phys Med Biol 41:2231–2249. doi:10.1088/0031-9155/41/11/001

    Article  Google Scholar 

  25. Yee KS (1966) Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media. IEEE Trans Antenn Propag 14(3):302–307. doi:10.1109/TAP.1966.1138693

    Article  Google Scholar 

  26. Berenger JP (1994) A perfectly macthed layer for the absorption of electromagnetic waves. Comput Phys 114:185–200. doi:10.1006/jcph.1994.1159

    Article  MATH  MathSciNet  Google Scholar 

  27. Jin J-M (1999) Electromagnetic analysis and design in magnetic resonance imaging. Biomedical engineering series. CRC Press, Boca Raton, xiv, p 282

  28. Collins CM et al (2005) Central brightening due to constructive interference with, without, and despite dielectric resonance. J Magn Reson Imaging 21(2):192–196. doi:10.1002/jmri.20245

    Article  Google Scholar 

  29. Blinkov SM, Glezer IëI (1968) The human brain in figures and tables; a quantitative handbook. Basic Books, New York, xxxii, p 482

    Google Scholar 

  30. Chrzanowska G, Beben A (1973) Weight of the brain and body height in man between the ages of 20 and 89 years. Folia Morphol (Warsz) 32(4):391–406

    Google Scholar 

  31. Debakan A, Sadowsky D (1978) Changes in brain weight during the span of human life: relation of brain weights and body heights and body weights. Ann Neurol 4:345–356. doi:10.1002/ana.410040410

    Article  Google Scholar 

  32. Hwang YI et al (1995) Study on the Korean adult cranial capacity. J Korean Med Sci 10:239–242

    Google Scholar 

  33. Miller A et al (1980) Spectral analysis of arterial bruits (phonoangiography): experimental validation. Circulation 61(3):515–520

    Google Scholar 

  34. Peters M, Jancke L, Zilles K (2000) Comparison of overall brain volume and midsagittal corpus callosum surface area as obtained from NMR scans and direct anatomical measures: a within-subject study on autopsy brains. Neuropsychologia 38(10):1375–1381. doi:10.1016/S0028-3932(00)00048-8

    Article  Google Scholar 

  35. Svennerholm L, Bostrom K, Jungbjer B (1997) Changes in weight and compositions of major membrane components of human brain during the span of adult human life of Swedes. Acta Neuropathol 94(4):345–352. doi:10.1007/s004010050717

    Article  Google Scholar 

  36. Caviness VS Jr et al (1995) Advanced application of magnetic resonance imaging in human brain science. Brain Dev 17(6):399–408. doi:10.1016/0387-7604(95)00090-9

    Article  Google Scholar 

  37. Kennedy DN et al (1998) Gyri of the human neocortex: an MRI-based analysis of volume and variance. Cereb Cortex 8(4):372–384. doi:10.1093/cercor/8.4.372

    Article  Google Scholar 

  38. Makris N et al (1999) MRI-Based topographic parcellation of human cerebral white matter and nuclei II. Rationale and applications with systematics of cerebral connectivity. Neuroimage 9(1):18–45. doi:10.1006/nimg.1998.0384

    Article  MathSciNet  Google Scholar 

  39. Kruggel F (2006) MRI-based volumetry of head compartments: normative values of healthy adults. Neuroimage 30(1):1–11. doi:10.1016/j.neuroimage.2005.09.063

    Article  Google Scholar 

  40. Visible_Human_Project (1996) The visible human project. US National Library of Medicine

  41. Aubert-Broche B, Evans AC, Collins L (2006) A new improved version of the realistic digital brain phantom. Neuroimage 32(1):138–145. doi:10.1016/j.neuroimage.2006.03.052

    Article  Google Scholar 

  42. Collins DL et al (1998) Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 17:463–468. doi:10.1109/42.712135

    Article  Google Scholar 

  43. Schnitzlein H, Murtagh F (1985) Imaging anatomy of the head and spine: a photographic color Atlas of MRI, CT, gross and microscopic anatomy in axial, coronal and sagital planes. Urban & Schwarzenberg, Munich

    Google Scholar 

  44. Bit-Babik G et al (2005) Simulation of exposure and SAR estimation for adult and child heads exposed to radiofrequency energy from portable communication devices. Radiat Res 163(5):580–590. doi:10.1667/RR3353

    Article  Google Scholar 

  45. Collins CM, Smith MB (2003) Spatial resolution of numerical models of man and calculated specific absorption rate using the FDTD method: a study at 64 MHz in a magnetic resonance imaging coil. J Magn Reson Imaging 18(3):383–388. doi:10.1002/jmri.10359

    Article  Google Scholar 

  46. NCRP (1981) Radiofrequency electromagnetic fields: properties, quantities and units, biophysical interaction, and measurement. National Council Radiation Protection and Measurements, Bethesda

    Google Scholar 

  47. Baker KB et al (2006) Variability in RF-induced heating of a deep brain stimulation implant across MR systems. J Magn Reson Imaging 24(6):1236–1242. doi:10.1002/jmri.20769

    Article  Google Scholar 

  48. Kong JA (1990) Electromagnetic wave theory, 2nd edn. Wiley, New York, xi, p 704

  49. Collins CM et al (2002) Numerical calculations of the static magnetic field in three-dimensional multi-tissue models of the human head. Magn Reson Imaging 20(5):413–424. doi:10.1016/S0730-725X(02)00507-6

    Article  Google Scholar 

  50. Fisel CR et al (1989) A general model for susceptibility-based MR contrast. In: Eighth annual meeting of the society of magnetic resonance in medicine. Amsterdam, The Netherlands

  51. Ibrahim TS et al (2001) Dielectric resonances and B(1) field inhomogeneity in UHFMRI: computational analysis and experimental findings. Magn Reson Imaging 19(2):219–226. doi:10.1016/S0730-725X(01)00300-9

    Article  Google Scholar 

  52. Foster KR et al (1979) Dielectric properties of brain tissue between 0.01 and 10 GHz. Phys Med Biol 24(6):1177–1187. doi:10.1088/0031-9155/24/6/008

    Article  Google Scholar 

  53. Gabriel C (2005) Dielectric properties of biological tissue: variation with age. Bioelectromagnetics Suppl 7:S12–S18

    Google Scholar 

  54. Foster KR, Schwan HP (1989) Dielectric properties of tissues and biological materials: a critical review. Crit Rev Biomed Eng 17(1):25–104

    Google Scholar 

  55. Peyman A et al (2007) Dielectric properties of porcine cerebrospinal tissues at microwave frequencies: in vivo, in vitro and systematic variation with age. Phys Med Biol 52(8):2229–2245. doi:10.1088/0031-9155/52/8/013

    Article  Google Scholar 

  56. Katscher U et al (2006) Electric properties tomography (EPT) via MRI. In: ISMRM Fourteenth Scientific Meeting. Seattle

  57. Hurt W, Ziriax J, Mason P (2000) Variability in EMF permittivity values: implications for SAR calculations. IEEE Trans Biomed Eng 47(3):396–401

    Article  Google Scholar 

  58. Mason P et al (2000) Effects of frequency, permittivity, and voxel size on predicted specific absorption rate values in biological tissue during electromagnetic—field exposure. IEEE Trans Microw Theory 48:2050–2058. doi:10.1109/22.884194

    Article  Google Scholar 

  59. Kang G, Gandhi OP (2004) Effect of dielectric properties on the peak 1 and 10 g SAR fro 802.11 a/b/g Frequencies 2.45 and 5.15 to 5.85 GHz. IEEE Trans Electromagn Compat 46:268–274. doi:10.1109/TEMC.2004.826875

    Article  Google Scholar 

  60. Van den Berg CAT (2006) Radiofrequency fields in hyperthermia and MRI. Exploiting their similarities for mutual benefits. Utrecht

  61. Clatz O et al (2007) Dynamic model of communicating hydrocephalus for surgery simulation. IEEE Trans Biomed Eng 54(4):755–758. doi:10.1109/TBME.2006.890146

    Article  Google Scholar 

  62. Greco WR (2007) New practices in computational modeling. Clin Cancer Res 13(4):1074–1075. doi:10.1158/1078-0432.CCR-06-2811

    Article  Google Scholar 

  63. Ayache N (ed) (2004) Computational models for the human body. In:Ciarlet P (ed) Handbook of numerical analysis. Elsevier, Amsterdam, p 670

  64. Coatrieux JL, Bassingthwaighte JB (eds) (2006) Special issue on the physiome and beyond. Proc IEEE 94:671–678. doi:10.1109/JPROC.2006.871765

    Google Scholar 

Download references

Acknowledgments

Preparation of this article was supported in part by grants from: The National Association for Research in Schizophrenia and Depression (NARSAD) and the National Institutes of Health National Center for Complementary and Alternative Medicine NCAM (NM); the Fairway Trust and NIH grants NS34189 & EB005149 (DK); R01 EB002459 and P41 RR014075 (GB), and the MIND institute. The authors would like to thank the many people that kindly contributed to this project with discussions and useful insights, including Franz Schmitt, Franz Hebrank and Andreas Potthast (Siemens Medical Systems), CK Chou and Goga Bit Babik (Motorola Corporate EME), Chris Collins (Penn State), David Kaplan, Sergio Fantini, Peter Wong, and Mark Cronin-Golomb (Tufts University). We would like also to thank the colleagues at the A. Martinos Center, including Martjin Cloos, Graham Wiggins, Mary Foley, and Larry Wald for their help with the MRI images, and Mr. George Papadimitriou and Mr. James Howard and for their contributions to this study.

Author information

Authors and Affiliations

  1. Departments of Psychiatry, Neurology and Radiology Services, Center for Morphometric Analysis, HST Athinoula A. Martinos Center, Harvard Medical School, Massachusetts General Hospital, Boston, MA, 02129, USA

    Nikos Makris, Seann Tulloch, Scott Sorg, Jonathan Kaiser & David Kennedy

  2. HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA, 02129, USA

    Nikos Makris, Leonardo Angelone, David Kennedy & Giorgio Bonmassar

  3. Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA

    Nikos Makris

  4. Biomedical Engineering Department, Tufts University, Medford, MA, 02155, USA

    Leonardo Angelone

Authors
  1. Nikos Makris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonardo Angelone
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seann Tulloch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Scott Sorg
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jonathan Kaiser
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David Kennedy
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Giorgio Bonmassar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giorgio Bonmassar.

Additional information

N. Makris and L. Angelone contributed equally to this work.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Makris, N., Angelone, L., Tulloch, S. et al. MRI-based anatomical model of the human head for specific absorption rate mapping. Med Biol Eng Comput 46, 1239–1251 (2008). https://doi.org/10.1007/s11517-008-0414-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-008-0414-z

Keywords

Search

Navigation