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The high number of cardiac diseases in the developed countries cause a strong interest in tools and technology for the early detection of any cardiac dysfunction. There, cardiac magnetic resonance imaging (MRI) plays a key role for the noninvasive examination of the beating heart. Unfortunately,In cardiac MR images the slice thickness is normally greater than the pixel size within the slices. In general, better segmentation and analysis results can be expected for isotropic highresolution (HR) data sets. If two orthogonal data sets, e. g. shortaxis (SA) and longaxis (LA) volumes are combined, an increase in resolution can be obtained. In this book we employ a superresolution reconstruction (SRR) algorithm for computing highresolution data sets from two orthogonal SA and LA volumes. We conclude that image quality is dramatically enhanced by applying an SRR technique especially for cardiac MR images where the resolution in sliceselection direction is about five times lower than within the slices.
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birth defects, or other reproductive harm.
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