This document describes a graphical user interface (GUI) developed for reconstructing magnetic resonance imaging (MRI) data using various algorithms. The GUI allows researchers to easily manipulate MRI data sets using three main reconstruction algorithms: SENSE, Conjugate Gradient SENSE, and Compressed Sensing. The GUI was created in MATLAB and provides adjustable input parameters, visualization of reconstruction processes, and output metrics to evaluate reconstruction quality. The goal is to provide an interactive platform for comparing different algorithms and reconstructing MRI images.

1. Advanced Image Reconstruction Algorithms in MRI
𝐴𝑏𝑏𝑎𝑠𝑅𝑎𝑧𝑎1
,𝑀𝑢𝑑𝑑𝑠𝑠𝑎𝑟 𝐴𝑏𝑏𝑎𝑠𝑖1
,𝑌𝑎𝑠𝑖𝑟 𝑇𝑎𝑟𝑖𝑞1
,𝑍𝑎𝑘𝑖 𝐴ℎ𝑚𝑒𝑑1
, Hammad Omer
Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad, Pakistan
Introduction:
Parallel imaging (PI) has the potential to reduce MR scan time by acquiring fewer k-space lines and uses multiple
receiver coils. There are many reconstruction algorithms available in parallel MRI which can be mainly categorized into
k-space and image domain methods. This work develops a comprehensive Graphical User Interface (GUI) for SENSE,
Conjugate Gradient SENSE (CG) and Compressed Sensing (CS) reconstruction algorithms. The proposed GUI environment
is a multiple window interface developed using MATLAB (R2012a). The GUI will provide the new researchers an easy and
interactive tool to manipulate data using various reconstruction algorithms in MRI.
METHODS:
1)SENSE [2] is a widely used method in PI. It works in the image domain and removes aliasing in the image obtained by
under-sampled k-space data using the knowledge of the receiver coil sensitivities. SENSE is described by:𝑨 = 𝑬𝑰(1)
𝐈 = 𝑬−𝟏
𝑨(2) Where A is the aliased image obtained from the under-sampled k-space; E is the encoding matrix which is
used to relocate the aliased signals to their proper locations in the reconstructed image and I is the image to be
recovered.
2) The basic principle of Conjugate Gradient SENSE [3] is based on equation (1) where an iterative solution of the
equation is found to get the reconstructed image I. 3)Sparsity of the image data in a transform domain is a necessary
condition for Compressed Sensing (CS)[1]. Sparser the data, fewer the number of measurements required for the
reconstruction and helps to achieve incoherent artifacts which can be removed easily using various iterative algorithms
[1] as part of CS. CS problem can be stated as: 𝒂𝒓𝒈𝒎𝒊𝒏 𝒎(∥ 𝓕 𝒖 𝒎 − 𝒚 ∥ 𝟐
𝟐
+ 𝝀 ∥ 𝜳𝒎 ∥ 𝟏)
(3)[1] Whereℱ𝑢 is the under-sampled Fourier operator, 𝑚 is our estimated vector, 𝑦 is the
measured k-space data from scanner,𝜆 being the regularization
parameter and 𝛹 the sparsifying operator. The sparsifying
transform used in this paper is the finite difference transform
usually referred as total variation (TV).The proposed GUI provides
the functions that are needed for SENSE, Conjugate Gradient
SENSE and Compressed Sensing MR image reconstruction.
Input Variables: 1.Aliased image 2.Sensitivity maps 3.Original
Image (optional) 4.Phase Encode direction 5.Acceleration Factor
6.no. of iterations and 7.k-spaceTrajectories e.g. Spiral, radial.
Outputs:1.Reconstructed Image 2. Mean g-Factor, standard
deviation of g-Factor and median g-factor 3.Artifact Power 4.
Signal-to-noise ratio (SNR) 5.g-factor map 6.Root mean square 7.
Estimated reconstruction time.
Conclusion: The proposed GUI provides an interactive platform for
researchers to reconstruct MR images using their data sets with
various reconstruction algorithms. Future work includes
adding more advanced MR reconstruction algorithms to the platform.
References:[1].M.Lustig, et al.(2011).Sparse MRI: The
Application of Compressed Sensing for Rapid MR
Imaging.USA,Wiley[2] Omer Hammad, et al ‘A Graphical
Generalized Implementation of SENSE reconstruction using
Matlab’ [3] Preussmann K P,et al , SENSE:sensitivity encodin ,g
for fast MRI, MRM, 1999, 42:952-962
Figure 1 GUI for MRI Reconstruction
Figure 2: Image Reconstructed through SENSE algorithm