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Patient Position Detection for SAR Optimization in Magnetic Resonance Imaging

  • Andreas Keil
  • Christian Wachinger
  • Gerhard Brinker
  • Stefan Thesen
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

Although magnetic resonance imaging is considered to be non-invasive, there is at least one effect on the patient which has to be monitored: The heating which is generated by absorbed radio frequency (RF) power. It is described using the specific absorption rate (SAR). In order to obey legal limits for these SAR values, the scanner’s duty cycle has to be adjusted. The limiting factor depends on the patient’s position with respect to the scanner. Detection of this position allows a better adjustment of the RF power resulting in an improved scan performance and image quality. In this paper, we propose real-time methods for accurately detecting the patient’s position with respect to the scanner. MR data of thirteen test persons acquired using a new “move during scan” protocol which provides low resolution MR data during the initial movement of the patient bed into the scanner, is used to validate the detection algorithm. When being integrated, our results would enable automatic SAR optimization within the usual acquisition workflow at no extra cost.

Keywords

Mahalanobis Distance Body Coil Radio Frequency Energy Visible Human Project Safe Magnetic Resonance Imaging 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    International Electrotechnical Comission: (IEC 60601 medical electrical equipment - part 2-33)Google Scholar
  2. 2.
    Zhai, Z., DeMeester, G.D., Morich, M.A., Harvey, P.R., Kleihorst, R.P.: Optimization of transmit efficiency for a T/R whole body coil at 3T. In: Proc. Int’l Soc. Mag. Reson. Med., vol. 13 (2005)Google Scholar
  3. 3.
    Zhai, Z., DeMeester, G.D., Shvartsman, S., Morich, M.A.: FDTD calculations of B1-field and SAR for 3T whole body coil. In: Proc. Int’l Soc. Mag. Reson. Med., vol. 10 (2002)Google Scholar
  4. 4.
    Ackerman, M.J.: The visible human project. Proc. of the IEEE 86(3), 504–511 (1998)CrossRefGoogle Scholar
  5. 5.
    Diehl, D., Weinert, U., Röckelein, R., Brinker, G.: SAR-calculations of a realistic, scalable human model in a MRI-resonator. In: Proc. Int’l IGTE Symposium on Numerical Field Calculation in Electrical Engineering (2002)Google Scholar
  6. 6.
    Pearson, K.: On lines and planes of closest fit to systems of points in space. Philosophical Mag. (1901)Google Scholar
  7. 7.
    Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. of Educational Psychology 24, 417–441, 498–520 (1933)Google Scholar
  8. 8.
    Kirby, M., Sirovich, L.: Application of the Karhunen-Loève procedure for the characterization of human faces. IEEE Trans. PAMI 12(1), 103–108 (1990)Google Scholar
  9. 9.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 586–591 (1991)Google Scholar
  10. 10.
    Yang, J., Zhang, D., Frangi, A.F., Yang, J.y.: Two-dimensional PCA: A new approach to appearance-based face representation and face recognition. IEEE Trans. PAMI 26(1), 131–137 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andreas Keil
    • 1
    • 3
  • Christian Wachinger
    • 1
  • Gerhard Brinker
    • 2
  • Stefan Thesen
    • 2
  • Nassir Navab
    • 1
  1. 1.Chair for Computer Aided Medical Procedures (CAMP)TU MunichGermany
  2. 2.Siemens Medical SolutionsErlangenGermany
  3. 3.Chirurgische Klinik und Poliklinik, Klinikum InnenstadtMunichGermany

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