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MRI Partial Fourier reconstruction with POCS

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5.0 | 1 rating Rate this file 18 Downloads (last 30 days) File Size: 11.1 KB File ID: #39350 Version: 1.2
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MRI Partial Fourier reconstruction with POCS



07 Dec 2012 (Updated )

Fast and robust reconstruction of Cartesian partial Fourier MRI data with POCS

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POCS (Projection Onto Convex Sets) is often used to reconstruct partial Fourier MRI data.
This implementation works with 2D or 3D data on a Cartesian grid. It is optimized for speed and automatically detects the asymmetrically sampled dimension.

Input data is generally assumed to be a multi-channel k-space signal, with the first dimension for the channels (or coils). You can, however, pass a pure 2D array.

 [im, kspFull] = pocs( kspIn, iter, watchProgr )

 === Input ===

   kspIn: Reduced Cartesian MRI Data-Set
               Any dimension may be reduced,
               but only one reduction dim. is allowed due to Physics/Math.

               Allowed shapes for kspIn are...
                 ... Ny x Nx
                 ... Nc x Ny x Nx
                 ... Nc x Ny x Nx x Nz

               With Nc == number of receive Channels / Coils.

               kspIn can either be a zero-padded array, so the partial Fourier property is obvious.
               Or kspIn can be the measured data only, then we try to find k-space centre automagically
               and create a zero-padded array with the full size, first.
               Errors are however more likely to occur in the latter case.

   iter: No. of iterations
   (optional) default: iter = 20
               Try on your own if larger iter improves your results!

   watchProgr: true/false; Whether the progress of the reconstruction should
   (optional) be monitored in an image window.
               In 3D data, only the central partition will be shown.

 === Output ===

   im: Reconstructed Images (channels not combined)

   kspFull: Reconstructed full k-space data (just the Fourier transformed im)

MATLAB release MATLAB 8.0 (R2012b)
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Comments and Ratings (1)
13 Sep 2016 Sen Jia  
14 Dec 2012 1.1

Smoothed transition between measured signal and reconstructed data to reduce Gibbs ringing.
Added example script

19 Dec 2012 1.2

* more loose dimension-detection
* better before/after screenshot

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