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Virtual magnetic resonance imaging - parallel implementation in a cluster computing environment

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, we present a virtual scanner of magnetic resonance imaging that aims at simplifying and accelerating methods of generating images. After an introduction to the subject of nuclear magnetic resonance and various approaches to the simulation of magnetic resonance imaging, details of the simulator are described. The proposed simulator consists of magnetization kernel (based on a solution of the Bloch equation), graphical user interface and module that performs calculation in a parallel environment. The package which parallelizes the magnetic resonance simulation is implemented on a computing cluster with the use of the Message Passing Interface standard. The parallel module can divide calculations related to different slices or different phase encoding steps between processors. The experimental results in the parallel environment show that it is possible to gain a significant speedup thus making it possible to acquire more exact images in a reasonable period of time.
Twórcy
autor
  • Politechnika Białostocka, Wydział Informatyki
Bibliografia
  • 1. Webb A.G.: Introduction to medical imaging, Wiley-IEEE Press, 2002.
  • 2. Bloch R, Hansen W.W., Packard M.: Nuclear induction. Physical Review 1946, 69, 127.
  • 3. Purcell E.M., Torrey H.C., Pound R.V.: Resonance absorption by nuclear magnetic moments in a solid. Phys. Rev. 1946, 69, 37.
  • 4. Bittoun J., Taquin J., Sauzade M.: A computer algorithm for the simulation of any nuclear magnetic resonance (NMR) imaging method. Magn. Reson. Imaging J 984, 2, 113-120.
  • 5. Summers R.M., Axel L., Israel S.: A computer simulation of nuclear magnetic resonance imaging. Magn. Reson. Med. 1986, 3, 363-376.
  • 6. Olsson M.B.E., Wirestam R., Persson B.R.R.: A computer simulation program for MR imaging: Application to RF and static magnetic field imperfections. Magn. Reson. Med. 1995, 34, 612-617.
  • 7. Brenner A.R., Kursch J., Noll T.G.: Parallelized high-performance MRI simulation on a workstation cluster, Proc. Int. Soc. Magn. Reson. Med. 1997, 3, 2052.
  • 8. Ortendahl D.A., Hylton N., Kaufman L., Watts J.C., Crooks L.E., Mills C.M., Stark D.D.: Analytical tools for magnetic resonance imaging. Radiology 1984, 153, 479-488.
  • 9. Bobman S.A., Riederer S.J., Lee J.N., Suddarth S.A., Wang H.Z., MacFall J.R.: Synthesized MRI images: comparison with acquired images. Radiology 1985, 155, 731-738.
  • 10. Petersson J.S., Christoffersson J.-O., Golman K.: MRI simulation using the k-space formalism. Mag. Reson. Imag. 1993, 11, 557-568.
  • 11. Kwan R.K.S., Evans A.C., Pike G.B.: MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans. Med. Imag. 1999, 18, 1085-1097.
  • 12. Benoit-Cattin H., Collewet G., Belaroussi B., Saint-Jalmes H., Odet C.: The SIMRI project: a versatile and interactive MRI simulator. J. Magn. Reson. 2005, 173, 97-115.
  • 13. Krętowski M., Rolland Y., Bezy-Wendling J., Coatrieux J.-L.: Physiologically based rnodeling for medical image analysis: application to 3D vascular network and CT scan angiography. IEEE Trans. Medical Imaging 2003, 22, 2, 248-257.
  • 14. Krętowski M., Bezy-Wendling J., Coupe P.: Simulation of biphasic CT findings in hepatic cellular carcinoma by a two-level physiological model. IEEE Trans. Biomed. Eng, 2007, 54, 3, 538-542.
  • 15. Lambert J.B., Mazzola E.P.: Nuclear magnetic resonance spectroscopy: an introduction to principles, applications, and experimental methods. Pearson Prentice Hall, New Jersey 2003.
  • 16. Krętowski M., Bezy-Wendling J.: Modeling for medical image analysis: framework and applications, In: C.T. Leondes (Ed.), Medical Imaging Systems Technology, World Scientific Publishing, 2005, 1-32.
  • 17. Kuperman V.: Magnetic resonance imaging - physical principles and applications, Academic Press, San Diego 2000.
  • 18. Bloch R: Nuclear induction. Physical Review 1946, 70, 460-474.
  • 19. Twieg D.: The k-trajectory formulation of the NMR imaging process with applications in analysis and synthesis of imaging methods. Med. Phys. 1983, 10, 5, 610-621.
  • 20. Press W.H., Teukolsky S.A., Vetterling W.T., Flannery B.P.: Numerical recipes in C. The art of scientific computing, Cambridge University Press, 1992.
  • 21. Gropp W., Lusk E., Skjellum A.: Using MPI: Portable parallel programming with the message-passing interface, MIT Press, 1999.
  • 22. Bezy-Wendling J., Krętowski M., Mescam M., Jurczuk K., Eliat P.-A.: Simulation of hepatocellular carcinoma in MRI by combined macrovascular and pharmacokinetic models. Proc. of 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Washington USA 2007, 1272-1275.
  • 23. Mescam M., Bezy-Wendling J., Krętowski M., Jurczuk K., Eliat P.-A., Olivie D.: Coupling texture analysis and physiological modeling for liver dynamic MRI interpretation, Proc. 24th IEEE EMBS, Lyon, France 2007, 4223-4226.
  • 24. Liu J., Wu J., Panda D.K.: High performance RDMA-based MPI implementation over InfiniBand. Int. Journal Parallel Program, 2004, 32, 3, 167-198.
  • 25. Juhasz Z., Kacsuk R, Kranzlmuller D.: Distributed and Parallel Systems: Cluster and Grid Computing, Springer, 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ3-0030-0040
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