Abstract
In this paper we present an integrated image registration algorithm for segmenting the heart muscle, the myocardium (MC). A sequence of magnetic resonance (MR) images of heart are acquired after injection of a contrast agent. An analysis of the perfusion of the contrast agent into myocardium is utilized to study its viability. Such a study requires segmentation of MC in each of the images acquired which is a difficult task due to rapidly changing contrast image the images. In this paper we present an information theoretic registration framework which integrates two channels of information, the pixel intensities and the local gradient information, to reliably and accurately segment the myocardium. In our framework, the physician hand draws contours representing the inner (the endocardium) and the outer (the epicardium) boundaries of the myocardium. These hand drawn contours are then propagated to the other images in the sequence of images acquired to segment the MC.
Chapter PDF
Similar content being viewed by others
Keywords
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
N. Al-Saadiet al. Noninvasive detection of myocardial ischemia from perfusion reserve based on cardiovascular magnetic resonance. Circulation, 101, 2000.
M. Breeuweret al. Automatic detection of the myocardial boundaries of the right and left ventricle in MR cardio perfusion scans. Proc. SPIE Med. Imag., Feb. 2001.
A. Collignon, F. Maes, et al. Automated multimodality image registration using information theory. Info. Proc. in Med. Imag. (IPMI), pages 263–274, 1995.
S. Geman and D. Geman. Stochastic relaxation, gibbs distributions and the bayesian restoration of images. IEEE Trans. PAMI, 6(6):721–741, Nov. 1984.
A. Hamadehet al. A unified approach to 3D-2D registration and 2D images segmentation. In H. U. Lemke, K. Inamura, C. C. Jaffe, and M. W. Vannier, editors, Computer assisted radiology, pages 1191–1196, 1995. Springer-Verlag.
J.J. Hopfield. Neurons with graded response have computational properties like those of two-state neurons. Proc. Natl. Acad. Sci., 81:3088–3092, 1984.
S.Z. Li. Markov Random Field Modeling in Computer Vision. Springer, 1995.
Jerosch H.M. et al. MR first pass imaging: quantitative assessment of transmural perfusion and collateral flow. Int. J. Card. Imaging, 13:205–218, 1997.
J. Pluim et al. Image registration by maximization of combined mutual information and gradient information. IEEE Trans. Med. Imag., 19(8), 2000.
Alexis Roche et al. Rigid registration of 3D ultrasound with MR images: a new approach combining intensity and gradient information. IEEE TMI, 2001.
Go RT et al. A prospective comparison of rubidium 82 PET and thallium 201 SPECT myocardial perfusion imaging utilizing a single dipyridamole stress in the diagnosis of coronary artery disease. J. Nucl. Med., 31:1899–1905, 1990.
M. Schwaiger. Myocardial perfusion imaging with PET. J. Nucl. Med., 35:693–698, 1994.
L. Spreeuwers et al. Automatic detection of myocardial boundaries in MR cardio perfusion images. MICCAI, 2001. M88.
P. Viola and W. M. Wells. Alignment by maximization of mutual information. Fifth Int. Conf. on Computer Vision, pages 16–23, 1995.
N. Wilke et al. Myocardial perfusion reserve: assessment with multisection, quantitative, first pass MR imaging. Radiology, 204:373–384, 1997.
A. Yullie. Energy functions for early vision and analog networks. Biol. Cybern, 61:115–124, 1989.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bansal, R., Funka-Lea, G. (2002). Integrated Image Registration for Cardiac MR Perfusion Data. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45786-0_81
Download citation
DOI: https://doi.org/10.1007/3-540-45786-0_81
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44224-0
Online ISBN: 978-3-540-45786-2
eBook Packages: Springer Book Archive