A Parallel Algorithm for Compressed Sensing Dynamic MRI Reconstruction
Loris Cannelli 1 , Paolo Scarponi 1 , Gesualdo Scutari 1 , and Leslie Ying 1
Electrical Engineering, University at
Buffalo, Buffalo, NY, United States
In this work we present a novel and very general
optimization algorithm customized for dynamic MRI
reconstruction under the compressed sensing framework.
The size of this kind of problems is usually huge: for
this reason is compulsory to design algorithms capable
to manage a large amount of data in an efficient way.
Our approach exploits the benefits of a parallel nature,
it relies on a smart decomposition of the original
problem and it also possesses the ability of recognizing
the elements that will be zero at the solution, taking
thus advantage of the sparse structure of the problem
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