Automatic Characterization of Myocardial Perfusion in Contrast Enhanced MRI

The use of contrast medium in cardiac MRI allows joining the high-resolution anatomical information provided by standard magnetic resonance with functional information obtained by means of the perfusion of contrast agent in myocardial tissues. The current approach to perfusion MRI characterization is the qualitative one, based on visual inspection of images. Moving to quantitative analysis requires extraction of numerical indices of myocardium perfusion by analysis of time/intensity curves related to the area of interest. The main problem in quantitative image sequence analysis is the heart movement, mainly due to patient respiration. We propose an automatic procedure based on image registration, segmentation of the myocardium, and extraction and analysis of time/intensity curves. The procedure requires a minimal user interaction, is robust with respect to the user input, and allows effective characterization of myocardial perfusion. The algorithm was tested on cardiac MR images acquired from voluntaries and in clinical routine.

[1]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[2]  I. Miller.  Probability, Random Variables, and Stochastic Processes , 1966 .

[3]  R. Coleman,et al.  MRI quantitative myocardial perfusion with compartmental analysis: A rest and stress study , 1997, Magnetic resonance in medicine.

[4]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[5]  R Kikinis,et al.  Semiautomated ROI analysis in dynamic MR studies. Part I: Image analysis tools for automatic correction of organ displacements. , 1991, Journal of computer assisted tomography.

[6]  N. Wilke,et al.  Assessing myocardial perfusion in coronary artery disease with magnetic resonance first-pass imaging. , 1998, Cardiology clinics.

[7]  Luigi Landini,et al.  A new algorithm for 3D automatic detection and tracking of cardiac wall motion , 1999, Computers in Cardiology 1999. Vol.26 (Cat. No.99CH37004).

[8]  Antonio Benassi,et al.  Automatic time sequence alignment in contrast enhanced MRI by maximization of mutual information , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  G. V. von Schulthess,et al.  Assessment of Myocardial Perfusion in Coronary Artery Disease by Magnetic Resonance: A Comparison With Positron Emission Tomography and Coronary Angiography , 2001, Circulation.

[10]  P. Bucy,et al.  American Association of Neurological Surgeons , 1986 .

[11]  J. Hajnal,et al.  A Registration and Interpolation Procedure for Subvoxel Matching of Serially Acquired MR Images , 1995, Journal of computer assisted tomography.

[12]  L. Bidaut,et al.  Automated registration of dynamic MR images for the quantification of myocardial perfusion , 2001, Journal of magnetic resonance imaging : JMRI.

[13]  Xin Yang,et al.  Computer aided measurement of local myocardial perfusion in MRI , 1993, Proceedings of Computers in Cardiology Conference.

[14]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[15]  M. Viergever,et al.  Medical image matching-a review with classification , 1993, IEEE Engineering in Medicine and Biology Magazine.

[16]  J. Udupa,et al.  An objective comparison of 3-D image interpolation methods , 1998, IEEE Transactions on Medical Imaging.

[17]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[18]  M. Fitzpatrick.  A Review of Medical Image Registration , 1993 .

[19]  W J Manning,et al.  Cardiac MRI for assessment of myocardial perfusion: Current status and future perspectives , 2001, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[20]  Ma Bin-rong,et al.  A review of medical image registration , 1999 .

[21]  Guy Marchal,et al.  Multi-modality image registration by maximization of mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[22]  Luigi Landini,et al.  Nonlinear anisotropic filtering as a tool for SNR enhancement in cardiovascular MRI , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[23]  S Napel,et al.  Functional imaging of the brain using computed tomography. , 1981, Radiology.