## 6 Discussion
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**SRR Procedure** & **N** & **M** & **MAE** & **SD** & **t** \\ \hline SR-no, MC-no & 100 & 29.12 & 0.01 & 1.614 & 0.101 \\ \hline SR-no, MC-yes & 100 & 25.21 & 0.01 & 0.612 & 0.303 \\ \hline cubic spline, MC-yes & 100 & 22.31 & 0.01 & 0.133 & 0.221 \\ \hline [PERSON] [50] & 100 & 16.23 & 0.01 & \(-\)0.354 & 0.431 \\ \hline [PERSON] [20] & 100 & 16.44 & 0.01 & \(-\)0.384 & 0.411 \\ \hline [PERSON] [51] & 100 & 16.22 & 0.01 & \(-\)0.416 & 0.431 \\ \hline [PERSON] no. 2 [43] & 100 & 15.55 & 0.01 & \(-\)0.565 & 0.554 \\ \hline [PERSON] et al. [52] & 100 & 14.31 & 0.04 & \(-\)0.136 & 0.644 \\ \hline [PERSON] et al. [53] & 100 & 15.21 & 0.02 & 0.112 & 0.199 \\ \hline [PERSON] et al. [54] & 100 & 16.12 & 0.03 & 0.823 & 0.422 \\ \hline [PERSON] et al. [55] & 100 & 19.92 & 0.02 & \(-\)0.901 & 0.390 \\ \hline [PERSON] et al. [17] & 100 & 18.41 & 0.01 & \(-\)0.774 & 0.111 \\ \hline The presented algorithm & 100 & 14.01 & 0.01 & \(-\)1.002 & 0.101 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The statistical data on the Mean Absolute Error (MAE) metrics for Figure 10. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**SRR Procedure** & **N** & **M** & **SD** & **t(99)** & \(\mathbf{p}\) \\ \hline SR-no, MC-no & 100 & 29.12 & 0.01 & 1.614 & 0.101 \\ \hline SR-no, MC-yes & 100 & 25.21 & 0.01 & 0.612 & 0.303 \\ \hline cubic spline, MC-yes & 100 & 22.31 & 0.01 & 0.133 & 0.221 \\ \hline [PERSON] [50] & 100 & 16.23 & 0.01 & \(-\)0.354 & 0.431 \\ \hline [PERSON] [20] & 100 & 16.44 & 0.01 & \(-\)0.384 & 0.411 \\ \hline [PERSON] [51] & 100 & 16.22 & 0.01 & \(-\)0.416 & 0.431 \\ \hline [PERSON] no. 2 [43] & 100 & 15.55 & 0.01 & \(-\)0.565 & 0.554 \\ \hline [PERSON] et al. [52] & 100 & 14.31 & 0.04 & \(-\)0.136 & 0.644 \\ \hline [PERSON] et al. [53] & 100 & 15.21 & 0.02 & 0.112 & 0.199 \\ \hline [PERSON] et al. [54] & 100 & 16.12 & 0.03 & 0.823 & 0.422 \\ \hline [PERSON] et al. [55] & 100 & 19.92 & 0.02 & \(-\)0.901 & 0.390 \\ \hline [PERSON] et al. [17] & 100 & 18.41 & 0.01 & \(-\)0.774 & 0.111 \\ \hline The presented algorithm & 100 & 14.01 & 0.01 & \(-\)1.002 & 0.101 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The statistical data regarding the IEM measures for Figure 10 are shown. \begin{table}
\begin{tabular}{c 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 & 13.72 & 24.55 & 1.13 \\ \hline
20 & 16.91 & 19.17 & 1.69 \\ \hline
30 & 19.44 & 19.42 & 1.80 \\ \hline
40 & 24.52 & 19.49 & 1.98 \\ \hline
50 & 25.91 & 17.92 & 2.59 \\ \hline
60 & 27.21 & 17.11 & 3.48 \\ \hline
70 & 29.22 & 16.33 & 3.54 \\ \hline
80 & 28.29 & 16.25 & 3.61 \\ \hline
90 & 31.71 & 15.54 & 3.77 \\ \hline
100 & 33.70 & 13.36 & 4.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The efficacy of the author’s approach is evaluated across various compression ratios of the k-space in relation to Figure 11.  
Figure 10: The results of the second phase of the clinical brain imaging trial. The current study focuses on the reconstruction of an image using various techniques. In the first row, the regular sampling scheme is utilized without motion correction and SRR applied (1) (PSNR = 21.26 dB). The reconstruction process involves the use of the B-spline curve (2) (PSNR = 23.29 dB), [PERSON]’s method [50] (3) (PSNR = 26.41 dB), [PERSON]’s method (4) (PSNR = 29.22 dB) as referenced in [20], [PERSON]’s procedure (5) (PSNR = 28.71 dB) as referenced in [51], and [PERSON]’s second algorithm (6) as referenced in [43] (PSNR = 29.89 dB). In the second row, [PERSON]’s method [52] (7) (PSNR = 29.14 dB) and [PERSON] et al.’s [53] procedure (8) (PSNR = 30.11 dB) are employed. Furthermore, the reconstruction procedure utilized [PERSON]’s approach [54] (9) (PSNR = 29.77 dB), [PERSON] et al.’s method [55] (10) (PSNR = 23.48 dB), [PERSON]’s method [17] (11) (12) (PSNR = 30.01 dB), as well as the author’s method (12) (PSNR = 32.61 dB).