Abstract
The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is studied and implemented with gridding algorithm in this paper. 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 un-aliased images from under-sampled data. The gridding algorithm is implemented with SENSE due to its ability in evaluating forward and adjoins operators with non-Cartesian sampled data. This paper also analyzes the performance of SENSE with real data set and identifies the computational issues that need to be improved for further research.
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
Since current MRI scanners already operate at the physical limit of data acquisition speed, mainly due to technical difficulties in producing rapidly switching magnetic field gradients, people resort to parallel imaging techniques that apply phased array coils and parallel reconstruction methods for faster MR imaging
In the two decades that followed, the MRI community has witnessed great progress in other parallel imaging schemes. Among these schemes perhaps the most well-known are SENSE presented by Pruessmann [1] and SMASH presented by Sodickson [2]
Summary
MRI is a relatively young technology, it has reached a juncture where future advances are limited by the scanning and reconstruction time. Liu unfold images from undersampled data, especially in the case of non-cartesian data sets Aimed at this problem, Pruessmann proposed an interative reconstruction scheme [3] 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. In this manner, the purpose of this term project is to implement the SENSE reconstruction scheme with non-cartesian k-space trajectories, and identify computational issues that need to be improved for further generalizing this algorithm for the reconstruction of three dimensional MRI dataset. The performance of SENSE with real data set and identifies the computational issues to be improved for research
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