Abstract #1560
Patch-based dictionaries for parallel MRI reconstruction
Jose Caballero 1 , Anthony N. Price 2,3 , Daniel Rueckert 1 , and Joseph V. Hajnal 2,3
1
Department of Computing, Imperial College
London, London, United Kingdom,
2
Division
of Imaging Sciences and Biomedical Engineering
Department, King's College London, London, United
Kingdom,
3
Centre
for the Developing Brain, King's College London, London,
United Kingdom
Acceleration of Magnetic Resonance (MR) acquisitions
through partially parallel imaging using array coils is
limited by noise amplification. Compressed sensing
regularization has de-noising properties that can
mitigate this effect. Recent results on dictionary
learning have shown that using overcomplete patch-based
frames and adapting them to the object can have a
notable impact on reconstruction by finding sparser
representations and adjusting to the natural features of
the object. However, these results have not yet been
tested for parallel MR. Here we propose an algorithm to
exploit overcomplete and adaptive frames for SPIRiT
reconstruction and demonstrate its superiority to
traditional wavelet regularization.
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