## 6 Discussion
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**SRA Procedure** & **N** & **M** & **SD** & **t** & \(p\) \\ \hline SR-no, MC-no & 100 & 1.40 & 0.01 & 1.112 & 0.202 \\ \hline SR-no, MC-yes & 100 & 1.55 & 0.01 & 0.292 & 0.172 \\ \hline cubic spline, MC-yes & 100 & 1.61 & 0.01 & 1.829 & 0.071 \\ \hline [PERSON] [50] & 100 & 2.23 & 0.01 & 0.179 & 0.221 \\ \hline [PERSON] [20] & 100 & 2.37 & 0.01 & \(-\)0.315 & 0.435 \\ \hline [PERSON] [51] & 100 & 2.66 & 0.01 & \(-\)0.371 & 0.441 \\ \hline [PERSON] no. 2 [43] & 100 & 2.40 & 0.01 & \(-\)0.410 & 0.424 \\ \hline [PERSON] et al. [52] & 100 & 4.11 & 0.04 & \(-\)0.140 & 0.545 \\ \hline [PERSON] et al. [53] & 100 & 4.01 & 0.01 & 0.013 & 0.876 \\ \hline [PERSON] et al. [54] & 100 & 3.76 & 0.03 & \(-\)0.541 & 0.211 \\ \hline [PERSON] et al. [55] et al. & 100 & 2.87 & 0.02 & \(-\)1.012 & 0.717 \\ \hline [PERSON] et al. [17] & 100 & 2.99 & 0.02 & 0.846 & 0.128 \\ \hline The proposed algorithm & 100 & 4.27 & 0.01 & \(-\)0.41 & 0.271 \\ \hline \hline \end{tabular}
\end{table} 
Table 26: The statistical data regarding the IEM measures for Figure 12 are shown. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**The Trajectory Used for Sampling in k-Space** & **N** & **M** & **SD** & **t(99)** & \(p\) \\ \hline The unaltered multi-blade k-space sampling technique & 100 & 17.02 & 0.01 & \(-\)1.701 & 0.058 \\ \hline The proposed algorithm & 100 & 13.01 & 0.00 & 1.130 & 0.233 \\ \hline \hline \end{tabular}
\end{table} 
Table 27: The statistical data regarding the Peak Signal-to-Noise Ratio (PSNR) metrics for Figure 12. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**The Trajectory Used for Sampling in k-Space** & **N** & **M** & **SD** & **t(99)** & \(p\) \\ \hline The unaltered multi-blade k-space sampling technique & 100 & 17.02 & 0.01 & \(-\)1.701 & 0.058 \\ \hline The proposed algorithm & 100 & 13.01 & 0.00 & 1.130 & 0.233 \\ \hline \hline \end{tabular}
\end{table} 
Table 28: The statistical data regarding the Mean Absolute Error (MAE) metrics for Figure 12. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Scanning Pattern** & **TR** & **TE** & **FOV** & **Voxel (mm)** & **Total Scan Duration (s)** & \(p\) \\ \hline P-R-O-P-E-L-L-E-R & 1200 & 180 & 290 & 0.96/0.96/1.00 & 366 & 0.142 \\ SENSE/ASSET & 1200 & 180 & 290 & 0.96/0.96/1.00 & 349 & 0.214 \\ GRAPPA/ARC & 1200 & 180 & 290 & 0.96/0.96/1.00 & 401 & 0.128 \\ The proposed algorithm & 1200 & 180 & 290 & 0.96/0.96/1.00 & 112 & 0.103 \\ \hline \hline \end{tabular}
\end{table} 
Table 30: The scanning parameters for Figure 10 are compared. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**The Trajectory Used for Sampling in k-Space** & **N** & **M** & **SD** & **t(99)** & \(p\) \\ \hline The unaltered multi-blade k-space sampling technique & 100 & 17.02 & 0.01 & \(-\)1.701 & 0.058 \\ \hline The proposed algorithm & 100 & 13.01 & 0.00 & 1.130 & 0.233 \\ \hline \hline \end{tabular}
\end{table} 
Table 29: The statistical data regarding the IEM measures for Figure 12 are shown. Moreover, the empirical data employed in this study consist of magnetic resonance (MR) images containing real values, which are distinct from the actual k-space data acquired during MRI scanning, complex-valued magnetic resonance (MR) pictures. Therefore, it is crucial to build a false connection between the input and output layers of Generative Adversarial Networks (GANs). Preprocessing is necessary for data that possess complicated values. In the future, there will be a heightened focus on the assessment and validation of radiologists' clinical effectiveness, particularly in relation to T1-weighted pictures and other magnetic resonance (MR) imaging techniques. This study introduces and illustrates some improvements that are designed to improve the quality of images while also minimizing the time required for acquisition. Although misregistration distortions are present, the presented algorithm has the ability to reduce artifacts that arise from data with a high degree of sparsity.