Abstract
Most automated methods for cardiac segmentation are not directly applicable to tagged MRI (tMRI) because they do not handle all of the analysis challenges: tags obscure heart boundaries, low contrast, image artifacts, and radial image planes. Other methods do not process all acquired tMRI data or do not ensure tissue incompressibility. In this chapter, we present a cardiac segmentation method for tMRI which requires no user input, suppresses image artifacts, extracts heart features using 3D grayscale morphology, and constructs a biventricular model from the data that ensures the near incompressibility of heart tissue. We project landmarks of 3D features along curves in the solution to a PDE, and embed biomechanical constraints using the finite element method. Testing on normal and diseased subjects yields an RMS segmentation accuracy of ∼ 2 mm, comparing favorably with manual segmentation, interexpert variability and segmentation methods for nontagged cine MRI.
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Axel L, Dougherty L. Heart wall motion: improved method of spatial modulation of magnetization for MR imaging. Radiology. 1989;172:349–350.
Zerhouni E, Parish D, Rogers W, Yang A, Shapiro E. Human heart: tagging with MR imaging-a method for non-invasive assessment of myocardial motion. Radiology. 1988;169:59–63.
Haber I, Metaxas D, Axel L. Three-dimensional motion reconstruction and analysis of the right ventricle using tagged MRI. Med Image Anal. 2000;4(4):335–355.
Tustison N, Amini A. Myocardial kinematics based on tagged MRI from volumetric NURBS models. In: SPIE. vol. 5369; 2003.
Young A, Axel L. Three-dimensional motion and deformation of the heart wall: estimation with spatial modulation of magnetization–a model-based approach. Radiology. 1992;185:241–247.
Osman N, Prince J. Angle images for measuring heart motion from tagged MRI. In: ICIP; 1998. p.704–8.
Young A, Fayad Z, Axel L. Right ventricular midwall surface motion and deformation using magnetic resonance tagging. Am J Physiol. 1996;271:H2677–H2688.
Hu Z, Metaxas D, Axel L. In vivo strain and stress estimation of the heart left and right ventricles from MRI images. Med Image Anal. 2003;7(4):435–444.
Dornier C, Ivancevic M, Lecoq G, Osman N, Foxall D, Righetti A, et al. Assessment of the left ventricle ejection fraction by MRI tagging. In: ISMRM; 2002.
Guttman M, Prince J, McVeigh E. Tag and Contour Detection in Tagged MR Images of the Left Ventricle. IEEE Trans Med Imaging. 1994;13(1):74–88.
Montillo A, Metaxas D, Axel L. Automated deformable model-based segmentation of the left and right ventricles in tagged cardiac MRI. In: MICCAI; 2003. p. 507–515.
Qian Z, Metaxas D, Axel L. Boosting and nonparametric based tracking of tagged MRI cardiac boundaries. In: MICCAI; 2006. p. 636–644.
Huang J, Huang X, Metaxas D, Axel L. Adaptive Metamorphs Model for 3D Medical Image Segmentation. In: MICCAI; 2007.
Sundar H, Davatzikos C, Biros G. Biomechanically-Constrained 4D Estimation of Myocardial Motion. In: MICCAI; 2009.
Chandrashekara R, Mohiaddin R, Razavi R, Rueckert D. Nonrigid Image Registration with Subdivision Lattices: Application to Cardiac MR Image Analysis. In: MICCAI; 2007.
Zhang S, Wang X, Metaxas D, Chen T, Axel L. LV surface reconstruction from sparse tMRI using laplacian surface deformation and optimization. In: ISBI; 2009. p. 698–701.
Yang L, Georgescu B, Zheng Y, Meer P, Comaniciu D. 3D ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers. In: CVPR; 2008.
Zhu Y, Papademetris X, Sinusas A, Duncan JS. Segmentation of myocardial volumes from real-time 3D echocardiography using an incompressibility constraint. MICCAI. 2007;10(Pt 1): 44–51.
Ecabert O, Peters J, Schramm H, Lorenz C, Von Berg J, Walker MJ, et al. Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging. 2008; 27(9):1189–1202.
Bistoquet A, Oshinski J, Skrinjar O. Myocardial deformation recovery from cine MRI using a nearly incompressible biventricular model. Med Image Anal. 2008;12(1):69–85.
Zhuge Y, Udupa J, Liu J, Saha P, Iwanaga T. A Scale-Based Method for Correcting Background Intensity Variation in Acquired Images. In: SPIE; 2002. p. 1103–1111.
Nyul L, Udupa J, Zhang X. New variants of a method of MRI scale standardization. IEEE Trans Med Imaging. 2000;19(2):143–150.
Herman G, Zheng J, Bucholtz C. Shape-based interpolation. IEEE Comput Graph. 1992;p. 69–80.
Xu C, Prince J. Generalized Gradient Vector Flow external forces for active contours. Sig Proc. 1998;71:131–9.
Cook R, Malkus D, M P. Concepts and applications of finite element analysis. Wiley; 1989.
Macneal R. Finite Elements: Their Design and Performance. Marcel Dekker; 1994.
Qian Z, Metaxas D, Axel L. Learning methods in segmentation of tMRI. ISBI; 2007. p. 688–691.
Herzka D, Guttman M, McVeigh E. Myocardial tagging with SSFP. Magn Reson Med. 2003;49(6):329–340.
Ryf S, Spiegel MA, Gerber M, Boesiger P. Myocardial tagging with 3D-CSPAMM. J of Mag Res Imaging. 2002;16:320–325.
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Montillo, A., Metaxas, D., Axel, L. (2011). Incompressible Biventricular Model Construction and Heart Segmentation of 4D Tagged MRI. In: Wittek, A., Nielsen, P., Miller, K. (eds) Computational Biomechanics for Medicine. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9619-0_15
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DOI: https://doi.org/10.1007/978-1-4419-9619-0_15
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