Abstract
The analysis of left ventricle (LV) wall motion is a critical step for understanding cardiac functioning mechanisms and clinical diagnosis of ventricular diseases. We present a novel approach for 3D motion modeling and analysis of LV wall in cardiac magnetic resonance imaging (MRI). First, a fully convolutional network (FCN) is deployed to initialize myocardium contours in 2D MR slices. Then, we propose an image registration algorithm to align MR slices in space and minimize the undesirable motion artifacts from inconsistent respiration. Finally, a 3D deformable model is applied to recover the shape and motion of myocardium wall. Utilizing the proposed approach, we can visually analyze 3D LV wall motion, evaluate cardiac global function, and diagnose ventricular diseases.
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Acknowledgments
This research has been supported by NIH grant (R01 HL 127661). We thank our colleagues from CBIM at Rutgers University who provided insight and expertise that greatly assisted the research, and Ms. Yan Chen for comments that greatly improved the manuscript.
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Yang, D., Wu, P., Tan, C., Pohl, K.M., Axel, L., Metaxas, D. (2017). 3D Motion Modeling and Reconstruction of Left Ventricle Wall in Cardiac MRI. In: Pop, M., Wright, G. (eds) Functional Imaging and Modelling of the Heart. FIMH 2017. Lecture Notes in Computer Science(), vol 10263. Springer, Cham. https://doi.org/10.1007/978-3-319-59448-4_46
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DOI: https://doi.org/10.1007/978-3-319-59448-4_46
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