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Abstract #1549

Novel Sampling Strategies for Sparse MR Image Reconstruction

Qiu Wang 1 , Michael Zenge 2 , Hasan Ertan Cetingul 1 , Edgar Mueller 2 , and Mariappan S Nadar 1

1 Imaging and Computer Vision, Siemens Corporation, Corporate Technology, Princeton, NJ, United States, 2 MR Application & Workflow Development, Siemens AG, Healthcare Sector, Erlangen, Germany

Compressed sensing or sparsity based MR reconstruction takes advantage of the fact that the image is compressible in a specific transform domain, and enables reconstruction based on under-sampled k-space data thereby reducing the acquisition time. One requirement for the compressed sensing theory to work is the data acquisition in k-space to be incoherent. Although many random sampling schemes theoretically meet such requirements good enough, the MR physics or even the pathophysiology of a patient might impose additional constraints which have to be taken into account. This is considered the coherence barrier. In the current work, we formulate a sampling strategy that promises to achieve asymptotic incoherence, thus breaking the coherence barrier. Please notice that both the data acquisition and the reconstruction which have been used are investigational prototypes which experience continuous development. Nonetheless, experimental results in a phantom and a volunteer demonstrate a significant improvement of the spatial resolution with an increasing sub-sampling rate and a constant data acquisition time accordingly.

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