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        "title": "Automatic Segmentation of Contrast Enhanced Cardiac MRI for Myocardial Perfusion Analysis",
        "date": "2008-03",
        "abstract": "Cardiovascular diseases are the major cause of death in the developed world. About\r\nhalf of these are due to ischemia heart diseases. The high death rate caused by\r\ncoronary artery diseases increases the need for preliminary detection. Perfusion\r\nmagnetic resonance imaging has turned out to be very promising for this purpose.\r\nA contrast agent is injected intravenously to visualize the perfusion. Due to the\r\nextremely time-consuming manual analysis of these relatively large datasets, efforts\r\nfor automatic approaches have been introduced. Most of these proposed methods\r\nfocus on parts of the analysis process. The present thesis identifies four steps for\r\nan automatic analysis approach: localization of the heart, suppression of motion\r\nartifacts, segmentation of the myocardium, and perfusion analysis.\r\nThis thesis presents a method covering all these subtasks in an automatic manner\r\nwith no need for any user interaction. First the acquired MR images are analyzed\r\nto roughly detect the heart. A registration step compensates motion artifacts based\r\non the breathing of the patient. The segmentation step provides the contour of the\r\nmyocardium at every time step. Based on these segmentations the perfusion is\r\nquantified.\r\nThis thesis gives a detailed description of the implementation. Furthermore\r\nthe algorithm was tested on 11 datasets. The obtained results are presented and\r\ndiscussed. Inspection of the results indicates that this method is very promising for\r\nan efficient perfusion analysis.",
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