Myocardial Delineation via Registration in a Polar Coordinate System

RATIONALE AND OBJECTIVES Cardiovascular disease is the number one cause of premature death in the western world. Analysis of cardiac function provides clinically useful diagnostic and prognostic information; however, manual analysis of function via delineation is prohibitively time consuming. This article describes a technique for analysis of dynamic magnetic resonance images of the left ventricle using a non-rigid registration algorithm. A manually delineated contour of a single phase was propagated through the dynamic sequence. MATERIALS AND METHODS Short-axis cine magnetic resonance images were resampled into polar coordinates before all the time frames were aligned using a non-rigid registration algorithm. The technique was tested on 10 patient data sets, a total of 1,052 images were analyzed. RESULTS Results of this approach were investigated and compared with manual delineation at all phases in the cardiac cycle, and with registration performed in a Cartesian coordinate system. The results correlated very well with manually delineated contours. CONCLUSION A novel approach to the registration and subsequent delineation of cardiac magnetic resonance images has been introduced. For the endocardium, the polar resampling technique correlated well with manual delineation, and better than for images registered without radial resampling in a Cartesian coordinate system. For the epicardium, the difference was not as apparent with both techniques correlating well.

[1]  J. Reiber,et al.  Comparison between manual and semiautomated analysis of left ventricular volume parameters from short-axis MR images. , 1997, Journal of computer assisted tomography.

[2]  Milan Sonka,et al.  Time-Continuous Segmentation of Cardiac Image Sequences Using Active Appearance Motion Models , 2001, IPMI.

[3]  Milan Sonka,et al.  Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images , 2001, IEEE Transactions on Medical Imaging.

[4]  T W Redpath,et al.  Determination of normal regional left ventricular function from cine-MR images using a semi-automated edge detection method. , 1999, Magnetic resonance imaging.

[5]  S Akselrod,et al.  Automatic assessment of cardiac function from short-axis MRI: procedure and clinical evaluation. , 1998, Magnetic resonance imaging.

[6]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[7]  Nicos Maglaveras,et al.  Model-based processing scheme for quantitative 4-D cardiac MRI analysis , 2002, IEEE Transactions on Information Technology in Biomedicine.

[8]  Russell M. Mersereau,et al.  Knowledge-based system for boundary detection of four-dimensional cardiac magnetic resonance image sequences , 1993, IEEE Trans. Medical Imaging.

[9]  Max A. Viergever,et al.  Geodesic deformable models for medical image analysis , 1998, IEEE Transactions on Medical Imaging.

[10]  Michael W. Vannier,et al.  Mathematical modeling of the heart using magnetic resonance imaging , 1992, IEEE Trans. Medical Imaging.

[11]  A. Boudraa,et al.  Automated detection of the left ventricular region in magnetic resonance images by Fuzzy C-Means model , 1997, The International Journal of Cardiac Imaging.

[12]  Haiying Liu,et al.  A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations , 2001, MICCAI.

[13]  Peter Boesiger,et al.  k‐t BLAST and k‐t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations , 2003, Magnetic resonance in medicine.

[14]  Milan Sonka,et al.  Segmentation of cardiac MR volume data using 3D active appearance models , 2002, SPIE Medical Imaging.

[15]  Ernesto Castillo,et al.  Cardiac MRI: Recent progress and continued challenges , 2002, Journal of magnetic resonance imaging : JMRI.

[16]  N. Duta,et al.  Segmentation of the left ventricle in cardiac MR images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[17]  James S. Duncan,et al.  Model-based deformable surface finding for medical images , 1996, IEEE Trans. Medical Imaging.

[18]  H. R. Singleton,et al.  Automatic cardiac MR image segmentation using edge detection by tissue classification in pixel neighborhoods , 1997, Magnetic resonance in medicine.

[19]  S Akselrod,et al.  An automatic contour extraction algorithm for short-axis cardiac magnetic resonance images. , 1996, Medical physics.

[20]  P. Clarysse,et al.  A FEM-based deformable model for the 3D segmentation and tracking of the heart in cardiac MRI , 2001, ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat..

[21]  N. Paragios A variational approach for the segmentation of the left ventricle in MR cardiac images , 2001, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision.

[22]  Valentin Fuster,et al.  Cardiac magnetic resonance imaging: a “one-stop-shop” evaluation of myocardial dysfunction , 2002, Current opinion in cardiology.

[23]  C Baldy,et al.  Automated myocardial edge detection from breath-hold cine-MR images: evaluation of left ventricular volumes and mass. , 1994, Magnetic resonance imaging.

[24]  A. Ardeshir Goshtasby,et al.  Segmentation of cardiac cine MR images for extraction of right and left ventricular chambers , 1995, IEEE Trans. Medical Imaging.

[25]  D J Pennell,et al.  Reduction in sample size for studies of remodeling in heart failure by the use of cardiovascular magnetic resonance. , 2000, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.

[26]  Steven R. Fleagle,et al.  Methods of graph searching for border detection in image sequences with applications to cardiac magnetic resonance imaging , 1995, IEEE Trans. Medical Imaging.

[27]  Debiao Li,et al.  Adaptive blood pool segmentation in three‐dimensions: Application to MR cardiac evaluation , 1996, Journal of magnetic resonance imaging : JMRI.

[28]  Alejandro F. Frangi,et al.  Three-dimensional modeling for functional analysis of cardiac images, a review , 2001, IEEE Transactions on Medical Imaging.

[29]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[30]  Milan Sonka,et al.  Active appearance motion model segmentation , 2001, Proceedings Second International Workshop on Digital and Computational Video.