Combining short-axis and long-axis cardiac MR images by applying a super-resolution reconstruction algorithm

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 high-resolution (HR) data sets. If two orthogonal data sets, e. g. short-axis (SA) and long-axis (LA) volumes are combined, an increase in resolution can be obtained. In this work we employ a super-resolution reconstruction (SRR) algorithm for computing high-resolution data sets from two orthogonal SA and LA volumes. In contrast to a simple averaging of both data in the overlapping region, we apply a maximum a posteriori approach. There, an observation model is employed for estimating an HR image that best reproduces the two low-resolution input data sets. For testing the SRR approach, we use clinical MRI data with an in-plane resolution of 1.5 mm×1.5 mm and a slice thickness of 8 mm. We show that the results obtained with our approach are superior to currently used averaging techniques. Due to the fact that the heart deforms over the cardiac cycle, we investigate further, how the replacement of a rigid registration by a deformable registration as preprocessing step improves the quality of the final HR image data. We conclude that image quality is dramatically enhanced by applying an SRR technique especially for cardiac MR images where the resolution in slice-selection direction is about five times lower than within the slices