A Matlab-Based Advance MR Image Reconstruction Package with Interactive Graphical User Interface

AR Shahid, Z Ahmed, A Raza, Y Tariq… - Applied Magnetic …, 2016 - Springer
AR Shahid, Z Ahmed, A Raza, Y Tariq, M Abbasi, H Omer
Applied Magnetic Resonance, 2016Springer
Parallel magnetic resonance imaging (PMRI) has the potential to reduce the MRI scan time
by acquiring fewer k-space lines using multiple independent receiver coils instead of a
single coil for data acquisition. Many advance reconstruction algorithms have been
proposed in the recent past to reconstruct fully sampled images from these undersampled
data. These algorithms are mainly categorized into k-space and image domain methods.
The work presented in this paper develops an interactive graphical user interface (GUI) tool …
Abstract
Parallel magnetic resonance imaging (PMRI) has the potential to reduce the MRI scan time by acquiring fewer k-space lines using multiple independent receiver coils instead of a single coil for data acquisition. Many advance reconstruction algorithms have been proposed in the recent past to reconstruct fully sampled images from these undersampled data. These algorithms are mainly categorized into k-space and image domain methods. The work presented in this paper develops an interactive graphical user interface (GUI) tool for a variety of advance reconstruction algorithms in MRI, namely, sensitivity encoding (SENSE) [1], conjugate gradient SENSE (CG-SENSE) [2], compressed sensing [3], and radial GRAPPA algorithms [4–7]. The proposed GUI tool environment is a multiple window interface developed using MATLAB (R2013a). The user has the option of loading the undersampled data in an interactive fashion, and image reconstruction can be performed using any of the available algorithms. The quality of the reconstructed images is evaluated using artifact power (AP) and SNR maps. This interactive platform is aimed at providing the researchers and medical practitioners an easy and interactive tool to test their data using various advance reconstruction algorithms in MRI.
Springer
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