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Abstract #0069

K-t Group Sparse using Intensity Based Clustering

Claudia Prieto1, Muhammad Usman1, Eike Nagel1, Philip Batchelor1, Tobias Schaeffter1

1Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom

K-t Group Sparse (k-t GS) has been recently introduced to achieve high acceleration factors in dynamic-MRI. Kt-GS exploits not just the sparsity of dynamic-MRI but also the spatial group structure of the x-f space. However, it presents two drawbacks: a) an additional training-scan is required for group assignment, and b) the group assignment is based only on the connectivity of neighbouring pixels using a time-consuming hard thresholding scheme. Here we propose to modify k-t GS by using the intensity order, estimated from the same acquired data, for a more robust group assignment. This approach has been tested in cine and perfusion cardiac images with acceleration factors up to 9.