Abstract
The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is implemented with non-cartesian sampled k-space trajectories in this paper. SENSE has the special capability to reduce the scanning time for MRI experiments while maintaining the image resolution with under-sampling data sets. In this manner, it has become an increasingly popular technique for multiple MRI data acquisition and image reconstruction schemes. The gridding algorithm is also implemented with SENSE due to its ability in evaluating forward and adjoin operator with non-cartesian sampled data. In this paper, the sensitivity map profile, field map information and the spiral k-space data collected from an array of receiver coils are used to reconstruct unaliased images from under-sampled data. The performance of SENSE with real data set identifies the computational issues to be improved for researched.
Highlights
magnetic resonance imaging (MRI) is a relatively young technology, it has reached a juncture where future advances are limited by the scanning and reconstruction time
The purpose of this term project is to implement the SENSE reconstruction scheme with non-cartesian k-space trajectories, evaluate its performance with “Double Vision” data set from the “ISMRM Reconstruction Challenge”, and identify computational issues that need to be improved for further generalizing this algorithm for the reconstruction of three dimensional MRI dataset
The k-space trajectories at the first and last interleaves are shown in Figure 2 and Figure 4
Summary
MRI is a relatively young technology, it has reached a juncture where future advances are limited by the scanning and reconstruction time. Despite the fact that these two techniques can significantly compress scanning time, usually computationally intensive inverse procedures are required to unfold images from under sampled data, especially in the case of non-cartesian data sets. Aimed at this problem, Pruessmann proposed an interative reconstruction scheme [4] that combines fast Fourier transform (FFT) with forward and inverse gridding operation. The computation complexity of SENSE is greatly reduced from O(N4) to O(N2logN) This method makes sensitivity-encoded imaging practical with arbitrary k-space acquisition pattern. The purpose of this term project is to implement the SENSE reconstruction scheme with non-cartesian k-space trajectories, evaluate its performance with “Double Vision” data set from the “ISMRM Reconstruction Challenge”, and identify computational issues that need to be improved for further generalizing this algorithm for the reconstruction of three dimensional MRI dataset
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