## 6 Discussion
\begin{table}
\begin{tabular}{l c c c c} \hline \multicolumn{2}{c}{**The Trajectory Used for Sampling in k-Space**} & **N** & **M** & **SD** & **t(99)** & \(p\)** \\ \hline The unaltered multi-blade k-space sampling technique & 100 & 2.04 & 0.01 & 0.139 & 0.869 \\ \hline The proposed algorithm & 100 & 4.34 & 0.00 & \(-\)1.268 & 0.262 \\ \hline \end{tabular}
\end{table} 
Table 8: The statistical data regarding the IEM measures for Figure 8 are shown. \begin{table}
\begin{tabular}{l c c c c} \hline \multicolumn{2}{c}{**The Trajectory Used for Sampling in k-Space**} & **N** & **M** & **SD** & **t(99)** & \(p\)** \\ \hline The unaltered multi-blade k-space sampling technique & 100 & 18.12 & 0.01 & \(-\)1.881 & 0.053 \\ \hline The proposed algorithm & 100 & 13.19 & 0.00 & 1.149 & 0.223 \\ \hline \end{tabular}
\end{table} 
Table 9: The statistical data regarding the IEM measures for Figure 8 are shown. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**SRR Procedure** & **N** & **M** & **SD** & **t** & \(\boldsymbol{p}\)** \\ \hline SR-no, MC-no & 100 & 21.26 & 0.01 & 1.091 & 0.271 \\ \hline SR-no, MC-yes & 100 & 24.51 & 0.01 & \(-\)0.779 & 0.432 \\ \hline cubic spline, MC-yes & 100 & 23.29 & 0.01 & 0.134 & 0.214 \\ \hline [PERSON] [50] & 100 & 26.41 & 0.01 & \(-\)0.311 & 0.499 \\ \hline [PERSON] [20] & 100 & 29.22 & 0.01 & \(-\)0.312 & 0.412 \\ \hline [PERSON] [51] & 100 & 28.71 & 0.01 & \(-\)0.411 & 0.417 \\ \hline [PERSON] no. 2 [43] & 100 & 29.89 & 0.02 & \(-\)0.513 & 0.544 \\ \hline [PERSON] et al. [52] & 100 & 29.14 & 0.01 & \(-\)0.234 & 0.883 \\ \hline [PERSON] et al. [53] & 100 & 30.11 & 0.04 & \(-\)0.02 & 0.112 \\ \hline [PERSON] et al. [54] & 100 & 29.77 & 0.01 & 0.521 & 0.239 \\ \hline [PERSON] et al. [55] & 100 & 23.48 & 0.04 & \(-\)1.012 & 0.131 \\ \hline [PERSON] et al. [17] & 100 & 30.01 & 0.03 & 0.771 & 0.124 \\ \hline The proposed algorithm & 100 & 32.61 & 0.01 & \(-\)0.548 & 0.158 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The statistical data on the Peak Signal-to-Noise Ratio (PSNR) metrics for Figure 10. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**The Remaining Proportion of the Original k-Space Samples [\%]** &
\begin{tabular}{c} **PSNR** \\ **[dB]** \\ \end{tabular} & **MAE** & **IEM** \\ \hline
10 & 14.89 & 23.61 & 1.21 \\ \hline
20 & 18.56 & 19.84 & 1.73 \\ \hline
30 & 20.16 & 19.28 & 1.81 \\ \hline
40 & 25.58 & 18.08 & 1.99 \\ \hline
50 & 26.43 & 17.61 & 2.77 \\ \hline
60 & 28.16 & 17.04 & 3.68 \\ \hline
70 & 29.17 & 16.21 & 3.69 \\ \hline
80 & 29.26 & 16.04 & 3.71 \\ \hline
90 & 30.21 & 15.62 & 3.82 \\ \hline
100 & 32.61 & 13.11 & 4.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The efficacy of the author’s approach is evaluated across various compression ratios of the k-space in relation to Figure 10. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**SRR Procedure** & **N** & **M** & **SD** & **t** & \(\mathbf{p}\) \\ \hline SR-no, MC-no & 100 & 1.52 & 0.01 & 1.112 & 0.202 \\ \hline SR-no, MC-yes & 100 & 1.61 & 0.01 & 0.292 & 0.171 \\ \hline cubic spline, MC-yes & 100 & 1.63 & 0.01 & 1.829 & 0.093 \\ \hline [PERSON] [50] & 100 & 2.23 & 0.01 & 0.179 & 0.244 \\ \hline [PERSON] [20] & 100 & 2.34 & 0.01 & \(-\)0.315 & 0.414 \\ \hline [PERSON] [51] & 100 & 2.23 & 0.01 & \(-\)0.371 & 0.439 \\ \hline [PERSON] no. 2 [43] & 100 & 2.39 & 0.01 & \(-\)0.410 & 0.401 \\ \hline [PERSON] et al. [52] & 100 & 4.02 & 0.04 & \(-\)0.140 & 0.553 \\ \hline [PERSON] et al. [53] & 100 & 4.02 & 0.01 & \(-\)0.013 & 0.816 \\ \hline [PERSON] et al. [54] & 100 & 3.66 & 0.03 & 0.541 & 0.411 \\ \hline [PERSON] et al. [55] & 100 & 2.66 & 0.02 & \(-\)1.012 & 0.817 \\ \hline [PERSON] et al. [17] & 100 & 2.67 & 0.02 & \(-\)0.846 & 0.128 \\ \hline The proposed algorithm & 100 & 4.36 & 0.01 & \(-\)0.41 & 0.271 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The statistical data regarding the Peak Signal-to-Noise Ratio (PSNR) metrics for Figure 10.