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Abstract #4501

Semi-Automated Segmentation of the Entire Left Ventricle from 3D Cine DENSE MRI Using Guide-Point Modeling

Daniel Alejandro Auger1, Xiaodong Zhong2, Frederick H. Epstein3, Ernesta M. Meintjes1, Bruce S. Spottiswoode4

1MRC/UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, Cape Town, Western Cape, South Africa; 2MR R&D Collaborations, Siemens Medical Solutions, Atlanta, GA, United States; 3Departments of Radiology and Biomedical Engineering, University of Virginia, Charlottesville, VA, United States; 4Cardiovascular MR R&D, Siemens Medical Solutions, Chicago, IL, United States

Demarcating the left ventricle (LV) from surrounding anatomical structures is an essentail step during the assessment of ventricular fucntion. Currently, this is done manually. This is time consuming, and the LV outline is left to the users interpretation. This work presents a semi-automatic segementation algorithm for 3D cine DENSE MRI. Using user defined guide points, a 3D LV geometrical model, and the inherent properties of cine DENSE, these methods improve the time required for segmentation by 10 fold, while showing promising results.