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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