## 5 Results and Discussion
### Discussion  
* **Discussion on encoder and decoder based on residual multi-attention:** To verify the efficacy of the key components in the proposed method, we evaluate three models in an ablation study. First, we evaluate the proposed residual multi-attention-based decoder-encoder model, in which we replace residual learning with traditional skip connections and remove the triple-attention mechanism. From the data in Table 4, we can see that, compared with configurations 1 and 2, without residual multi-attention (RMA), the PSNR of T1 WI decreases by 2.974 dB and the SSIM decreases by 0.037; similarly, the PSNR of T2 WI decreases by 5.964 dB and the SSIM decreases by 0.08. It can also be observed in Figure 12 that the reconstruction error of configuration 2 is reduced compared to configuration 1 which does not integrate any components, and the restored image details are clearer. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & \multirow{2}{*}{**RMA**} & \multirow{2}{*}{**MMFIM**} & \multirow{2}{*}{**SMFIM**} & \multirow{2}{*}{**T1 WI**} & \multicolumn{6}{c}{**fastMRI Brain**} \\ \cline{5-10}  & & & & & **T1 WI** & & & **T2 WI** & \\ \cline{5-10}  & & & & **PSNR \(\uparrow\)3** & **SSIM\(\uparrow\)** & **RLNE\(\downarrow\)4** & **PSNR \(\uparrow\)** & **SSIM\(\uparrow\)** & **RLNE\(\downarrow\)** \\ \hline \hline \multicolumn{10}{c}{1D Random 8\(\times\) (Mean)} \\ \hline ZF & \(\times\)1 & \(\times\)1 & \(\times\) & 22.572 & 0.646 & 0.300 & 21.764 & 0.673 & 0.310 \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & 33.599 & 0.902 & 0.074 & 27.261 & 0.856 & 0.164 \\ \hline
2 & \(\surd\)2 & \(\times\) & \(\times\) & 36.573 & 0.939 & 0.053 & 33.225 & 0.936 & 0.083 \\ \hline
3 & \(\times\) & \(\surd\) & \(\surd\) & 34.381 & 0.907 & 0.068 & 30.599 & 0.908 & 0.112 \\ \hline MHCFIN & \(\surd\) & \(\surd\) & \(\surd\) & **39.896** & **0.964** & **0.036** & **35.235** & **0.948** & **0.065** \\ \hline \hline \end{tabular}  
* add the module, 2 remove the module. 3 the higher the better, 4 the lower the better. The best results are labeled in bold.  
\end{table} 
Table 4: Ablation study on different variant methods on fastMRI brain datasets with the 1D random undersampling pattern at acceleration rate 8\(\times\). Figure 11: The training loss of different reconstruction methods on the fastMRI brain dataset. **(b)**: **Discussion on the multi-modal feature interaction module and single-modal feature fusion module:** Since the multi-modal fusion interaction module and the single-modal feature fusion module are used jointly, we discuss their effects together in the ablation study. As can be seen from the data in Table 4, compared with configuration 1 without using these feature fusion technologies, configuration 3 has the PSNR of T1 WI increased by 0.782 dB, SSIM increased by 0.005, and RLNE decreased with the help of multi-modal feature interactive learning by 0.006; the improvement of T2 WI is more significant, as PSNR increases by 3.338 dB, SSIM increases by 0.052, and RLNE decreases by 0.052. Figure 12 also clearly shows that, after the introduction of MMFIM and SMFFM, the overall reconstruction error is reduced; especially, the improvement on T2 WI is particularly obvious. Based on the introduction of RMA, the difference between MMFIM and SMFFM is particularly significant. By comparing configuration MHCFIN with configuration 2, it can be seen that the PSNR of T1 WI has increased by 3.323 dB, and the PSNR of T2 WI has increased by 2.01 dB. This result verifies the effectiveness of our proposed components, and when these components are used jointly, they can bring better results than when used alone. At the same time, learning complementary information between different modalities can also further improve the quality of reconstruction.