## 5 Results and Discussion
### Qualitative Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{7}{c}{**fastMRI Knee**} \\ \hline
**Methods** & \multicolumn{3}{c}{**PDs**} & \multicolumn{3}{c}{**PDFSs**} \\ \hline
**PSNR \(\uparrow\) 3** & **SSIM \(\uparrow\)** & **RLNE \(\downarrow\) 4** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **RLNE \(\downarrow\)** \\ \hline \multicolumn{7}{c}{1D Random 10\(\times\) (Mean (Standard))} \\ \hline ZF 1 & 23.822 (1.271) & 0.648 (0.047) & 0.383 (0.065) & 27.492 (1.725) & 0.734 (0.033) & 0.314 (0.046) \\ \hline U-Net [51] & 32.409 (1.312) & 0.849 (0.039) & 0.142 (0.023) & 33.731 (1.863) & 0.871 (0.024) & 0.153 (0.021) \\ \hline STUN [15] & 33.927 (1.372) & 0.856 (0.039) & 0.120 (0.024) & 37.427 (1.933) & 0.905 (0.021) & 0.100 (0.019) \\ \hline PC-UNet [47] & 32.464 (1.653) & 0.847 (0.050) & 0.142 (0.028) & 34.758 (1.568) & 0.893 (0.022) & 0.136 (0.021) \\ \hline MINet [49] & 29.454 (1.376) & 0.773 (0.043) & 0.201 (0.038) & 32.367 (1.941) & 0.834 (0.032) & 0.179 (0.027) \\ \hline DuDoCAF [27] & 35.327 (1.363) & 0.897 (0.027) & 0.102 (0.020) & 37.297 (2.148) & 0.926 (0.019) & 0.102 (0.019) \\ \hline DuDoNet [14] & 33.915 (1.206) & 0.873 (0.033) & 0.119 (0.015) & 34.962 (1.516) & 0.874 (0.024) & 0.132 (0.014) \\ \hline MHCFIN 2 & **35.877 (1.709)** & **0.921 (0.025)** & **0.096 (0.023)** & **38.256 (2.236)** & **0.943 (0.015)** & **0.092 (0.018)** \\ \hline \multicolumn{7}{c}{1D Random 12\(\times\) (Mean (Standard))} \\ \hline ZF & 22.077 (1.069) & 0.614 (0.041) & 0.464 (0.052) & 25.535 (1.651) & 0.682 (0.037) & 0.391 (0.040) \\ \hline U-Net [51] & 30.853 (1.205) & 0.828 (0.046) & 0.170 (0.024) & 32.285 (1.622) & 0.852 (0.023) & 0.180 (0.020) \\ \hline STUN [15] & 32.466 (1.523) & 0.790 (0.045) & 0.142 (0.026) & 35.886 (1.943) & 0.888 (0.024) & 0.120 (0.021) \\ \hline PC-UNet [47] & 31.113 (1.544) & 0.820 (0.051) & 0.166 (0.031) & 33.459 (1.812) & 0.856 (0.030) & 0.157 (0.016) \\ \hline MINet [49] & 28.830 (1.814) & 0.777 (0.042) & 0.218 (0.052) & 31.799 (1.797) & 0.837 (0.030) & 0.191 (0.029) \\ \hline DuDoCAF [27] & 34.093 (1.472) & 0.879 (0.035) & 0.118 (0.025) & 36.896 (2.560) & 0.925 (0.021) & 0.108 (0.022) \\ \hline DuDoNet [14] & 32.365 (1.266) & 0.859 (0.032) & 0.142 (0.022) & 33.772 (1.633) & 0.865 (0.025) & 0.152 (0.018) \\ \hline MHCFIN & **34.132 (1.748)** & **0.892 (0.024)** & **0.117 (0.027)** & **37.771 (2.790)** & **0.936 (0.018)** & **0.099 (0.028)** \\ \hline \end{tabular}  
* \({}^{1}\) zero-filled, 2 multi-hierarchical complementary feature interaction network, 3 the higher the better, 4 the lower the better. The best results are labeled in bold.  
\end{table} 
Table 3: Quantitative comparison of different reconstruction methods on fastMRI knee datasets in 1D random undersampling pattern with two acceleration factors. Figure 8: Qualitative comparison with different reconstruction methods using 1D equispaced undersampling pattern with acceleration factor 12\(\times\) on fastMRI brain datasets. Figure 7: Qualitative comparison with different reconstruction methods using 1D random undersampling pattern with acceleration factor 8\(\times\) on fastMRI brain datasets. Ground truth (GT), zero-filled (ZF), reconstructed MR images (T1 WI and T2 WI), error maps, and zoomed-in details are provided. Figure 10 shows the performance evaluation of different reconstruction methods on the fastMRI brain dataset using an \(8\times\) acceleration factor in the form of a bar graph. Part (a) shows the PSNR value, part (b) shows the SSIM value and part (c) reflects the RLNE value. The standard deviation of the performance of each method is represented by a black arrow, attached to the corresponding bar graph. The observation results show that, in parts (a) and (b), our proposed reconstruction method shows good performance on T1 WI and T2 WI data, outperforming other methods. In addition, in part (c), the RLNE value of our method is the lowest, further confirming its high accuracy in data reconstruction.