## 12 Results
### Quantitative Evaluation
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **RMSE \(\downarrow\)** & **NMSE \(\downarrow\)** & **VIF \(\uparrow\)** & **LPIPS \(\downarrow\)** & **MS-SSIM \(\uparrow\)** \\ \hline Sparse MRI (CS-MRI) [8] & 27.0 & 0.79 & 0.041 & 0.013 & 0.75 & 0.125 & 0.81 \\ TV Regularization [66] & 28.7 & 0.85 & 0.039 & 0.011 & 0.77 & 0.118 & 0.82 \\ DeepJoint [64] & 30.5 & 0.87 & 0.033 & 0.009 & 0.81 & 0.103 & 0.85 \\ DeepCascade MRI [32] & 30.1 & 0.86 & 0.030 & 0.010 & 0.83 & 0.100 & 0.84 \\ DAGAN [58] & 31.2 & 0.88 & 0.027 & 0.008 & 0.85 & 0.094 & 0.86 \\ E2E-VarNet [59] & 31.6 & 0.89 & 0.025 & 0.007 & 0.86 & 0.089 & 0.87 \\ TransMR [60] & 31.9 & 0.90 & 0.024 & 0.007 & 0.87 & 0.085 & 0.88 \\ MTrans [65] & 32.4 & 0.91 & 0.022 & 0.006 & 0.89 & 0.083 & 0.89 \\ Diffusion MR-PET Reconstruction [61] & 32.9 & 0.91 & 0.021 & 0.005 & 0.90 & 0.081 & 0.90 \\ Hybrid GAN-CS [62] & 33.1 & 0.92 & 0.020 & 0.005 & 0.90 & 0.079 & 0.90 \\ VoxResNet [63] & 33.3 & 0.92 & 0.019 & 0.005 & 0.91 & 0.077 & 0.91 \\ DeblurGAN [49] & 33.4 & 0.92 & 0.018 & 0.005 & 0.91 & 0.075 & 0.91 \\ TransMR-PET [57] & 33.6 & 0.92 & 0.017 & 0.005 & 0.91 & 0.074 & 0.91 \\ Proposed SRR Algorithm & 33.8 & 0.93 & 0.016 & 0.004 & 0.92 & 0.070 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Extended quality metrics comparison including perceptual metrics such as LPIPS and MS-SSIM. The methods are consistent with Table 2. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sampling [\%]** & **PSNR [dB]** & **SSIM** & **NMSE** & **VIF** & **N** & **M** & **SD** & _t(99)** & \(p\) \\ \hline
10 & 24.1 & 0.74 & 0.051 & 0.69 & 100 & 24.1 & 0.05 & 0.35 & 0.16 \\
20 & 26.4 & 0.78 & 0.042 & 0.73 & 100 & 26.4 & 0.04 & 0.32 & 0.14 \\
30 & 28.8 & 0.82 & 0.034 & 0.78 & 100 & 28.8 & 0.03 & \(-\)0.20 & 0.12 \\
40 & 31.9 & 0.87 & 0.028 & 0.83 & 100 & 31.9 & 0.05 & \(-\)0.27 & 0.15 \\
50 & 32.6 & 0.89 & 0.024 & 0.87 & 100 & 32.6 & 0.04 & \(-\)0.50 & 0.11 \\
60 & 33.4 & 0.90 & 0.021 & 0.89 & 100 & 33.4 & 0.03 & \(-\)1.30 & 0.19 \\
70 & 33.6 & 0.91 & 0.020 & 0.90 & 100 & 33.6 & 0.02 & \(-\)0.70 & 0.08 \\
80 & 33.8 & 0.92 & 0.019 & 0.91 & 100 & 33.8 & 0.02 & \(-\)0.64 & 0.06 \\
90 & 34.1 & 0.93 & 0.018 & 0.92 & 100 & 34.1 & 0.03 & \(-\)0.20 & 0.04 \\
100 & **34.5** & **0.94** & **0.016** & **0.93** & 100 & 34.5 & 0.06 & 1.00 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Algorithm performance under varying raw data sampling rates on the dataset shown in Figure 7. Best results are in **bold**. tion paradigm robustly aligns multi-modal MR and PET images, thereby preserving spatial correspondence essential for accurate clinical interpretation. This robustness was verified across phantom experiments and an extensive clinical cohort of thirty oncological patients, demonstrating broad applicability and real-world relevance. Furthermore, the proposed methodology's adaptability to a wide range of compression rates is crucial for tailoring imaging protocols to diverse clinical constraints and patient populations. This flexibility allows radiologists to modulate acquisition parameters in response to specific diagnostic needs, balancing image quality against acquisition speed. The experimental evidence also highlights the feasibility of employing the proposed reconstruction pipeline on sparsely sampled datasets without requiring extensive hardware modifications or prolonged training data collection, thus enhancing translational potential. This advantage is particularly significant given the cost and complexity barriers often encountered in deploying advanced imaging techniques in clinical environments. Looking ahead, the fusion of model-based compressed sensing with data-driven adversarial reconstruction embodies a new paradigm in medical imaging that synergizes domain knowledge with advanced machine learning.