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Application of image registration techniques in dynamic magnetic resonance imaging of breast

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a relatively new, promising technique for breast cancer diagnostics. A few series of images of the same body region are rapidly acquired before, during and after injection of paramagnetic contrast agent. Propagation of the contrast agent causes modification of MR signal over time. Its analysis provides information on tissue properties, including tumour status, that is not available with the regular MRI. Unintentional patient's movements during the examination result with incorrect alignment of the consecutive image series. Their analysis is then difficult, inaccurate or even impossible. The purpose of this work is to design a registration scheme that could be applied to solve the problem in a routine manner, in standard hospital conditions. The proposed registration framework, composed of B-spline transformation, mean squares metric and LBFGSB optimizer, is able to produce satisfactory results within reasonable time.
Rocznik
Tom
Strony
275--280
Opis fizyczny
Bibliogr. 13 poz., rys.
Twórcy
  • Maria Curie-Skłodowska University, pl. M. Curie-Skłodowskiej 1, 20-031 Lublin, Poland
autor
Bibliografia
  • [1] World Health Organization, Women's health, Fact sheet No. 334, November 2009, http://www.who.int/mediacentre/factsheets/fs334/en/.
  • [2] OJEDA-FOURNIER H., COMSTOCK C.E., MRI for breast cancer: Current indications, Indian J. Radiol. Imaging 2009, Vol. 19, pp. 161-9.
  • [3] LI X., DAWANT B.M., WELCH E.B., CHAKRAVARTHY A.B., FREEHARDT D., MAYER I., KELLEY M., MESZOELY I., GORE J.C., YANKEELOV T.E., A Nonrigid Registration Algorithm for Longitudinal Breast MR Images and the Analysis of Breast Tumor Response, Magn. Reson. Imaging, Vol. 27, No. 9, November 2009, pp. 1258-1270.
  • [4] VIOLA P., WELLS W.M., Alignment by Maximization of Mutual Information, International Journal of Computer Vision, Vol. 24, No. 2, 1997, pp. 137-154.
  • [5] ZITOVA B., FLUSSER J., Image registration methods: a survey, Image and Vision Computing 21, 2003, pp. 977-1000.
  • [6] MAINTZ J.B.A., VIERGEVER M.A., A Survey of Medical Image Registration, Medical Image Analysis, Vol. 2, No. 1, 1998, pp. 1-37.
  • [7] GOSHTASBY A., 2-D and 3-D Image Registration for Medical, Remote Sensing and Industrial Applications, Wiley Press, 2005.
  • [8] KUCZYŃSKI K., MIKOŁAJCZAK P., Information theory based medical image processing, Opto-Electronics Review, Vol. 11, No. 3, Warsaw 2003, pp. 253-259.
  • [9] UNSER M., Splines: A Perfect Fit for Signal and Image Processing, IEEE Signal Processing Magazine, Vol. 16, No. 6, November 1999, pp. 22-38.
  • [10] RUECKERT D., SONODA L.I., HAYES C., HILL D.L.G., LEACH M.O., HAWKES D.J., Nonrigid registration using free-form deformations: Application to breast mr images, IEEE Transaction on Medical Imaging, Vol. 18, No. 8, 1999, pp. 712-721.
  • [11] MATTES D., HAYNOR D.R., VESSELLE H., LEWELLEN T.K., EUBANK W., PET-CT image registration in the chest using free-form deformations, IEEE Trans. on Medical Imaging, 22(1), January 2003, pp. 120-128.
  • [12] IBANEZ L., SCHROEDER W., NG L., CATES J., The ITK Software Guide, Second Edition, Updated for ITK ver. 2.4, 2005, http://www.itk.org.
  • [13] BYRD R.H., ZHU C., NOCEDAL J., L-bfgs-b: Algorithm778: L-bfgs-b, fortran routines for large scale bound constrained optimization, ACM Transactions on Mathematical Software, Vol. 23, No. 4, November 1997, pp. 550–560.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0016-0032
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