## 3 Results  
The accuracy of the PWVs from the ground truth images was evaluated according to Gaussian noise levels. The vertical projection method showed an average difference of 3.400 \(\pm\) 0.280 m/s, regardless of the noise level. By contrast, the radial projection method exhibited an average difference of 1.644 \(\pm\) 0.798 m/s. The MAE in the radial projection method increased as the noise level increased (Table 1), and the average MAEs in both hemispheres were 0.397 and 0.647 m/s at noise levels of 10 and 20%, 1.016 and 1.686 m/s at 30 and 60%, and 2.045 and 2.816 m/s at 70 and 100%, respectively. However, these differences were much smaller than the MAE in the vertical projection method. Furthermore, the PWVs were significantly different between the ground truth, vertical projection, and radial projection across different Gaussian noise levels (\(p\) < 0.05), except for in the left hemisphere at the 10%, 20%, 30%, and 80% noise level (Table 2). Post-hoc tests using DSCF pairwise comparisons indicated that radial projection PWVs were not significantly different from ground truth PWVs, except for in the left hemisphere at the 90% noise level and right hemisphere at the 100% noise level. By contrast, the vertical-projection PWVs were significantly different from the ground truth PWVs in all cases, except for in the left hemisphere at the 10% and 20% noise levels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Noise** \\ **Levels** \\ \end{tabular} } & \multicolumn{2}{c}{**MAE a** Mean \(\pm\) SD (m/s)} \\  & \multicolumn{2}{c}{**Radial Projection**} & \multicolumn{2}{c}{**Vertical Projection**} \\  & **Left** & **Right** & **Left** & **Right** \\ \hline
10\% & 0.30 \(\pm\) 0.12 & 0.49 \(\pm\) 0.28 & 3.95 \(\pm\) 1.57 & 3.03 \(\pm\) 1.11 \\
20\% & 0.55 \(\pm\) 0.32 & 0.74 \(\pm\) 0.34 & 3.27 \(\pm\) 0.93 & 3.37 \(\pm\) 1.22 \\
30\% & 1.09 \(\pm\) 0.64 & 0.94 \(\pm\) 0.79 & 3.47 \(\pm\) 1.33 & 3.00 \(\pm\) 0.98 \\
40\% & 1.24 \(\pm\) 0.55 & 1.73 \(\pm\) 1.10 & 3.62 \(\pm\) 0.81 & 3.31 \(\pm\) 1.06 \\
50\% & 1.49 \(\pm\) 1.00 & 1.60 \(\pm\) 0.96 & 3.77 \(\pm\) 1.08 & 3.09 \(\pm\) 0.87 \\
60\% & 1.26 \(\pm\) 0.67 & 2.12 \(\pm\) 1.31 & 3.97 \(\pm\) 1.43 & 3.58 \(\pm\) 1.06 \\
70\% & 2.27 \(\pm\) 1.41 & 2.28 \(\pm\) 1.38 & 3.38 \(\pm\) 1.70 & 3.18 \(\pm\) 0.68 \\
80\% & 2.02 \(\pm\) 0.97 & 2.07 \(\pm\) 0.82 & 3.21 \(\pm\) 1.46 & 3.33 \(\pm\) 1.27 \\
90\% & 3.10 \(\pm\) 1.66 & 2.54 \(\pm\) 1.38 & 3.42 \(\pm\) 1.16 & 3.22 \(\pm\) 1.06 \\
100\% & 2.45 \(\pm\) 1.29 & 2.61 \(\pm\) 1.21 & 3.64 \(\pm\) 0.76 & 3.21 \(\pm\) 1.01 \\ Mean \(\pm\) SD (m/s) & 1.64 \(\pm\) 0.80 & & 3.40 \(\pm\) 0.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy evaluation of the methods using the MAE of PWVs. Finally, the feasibility of the proposed technique was evaluated using the differences in PWVs between the simulation, undersampled, and interpolated datasets, and there was a significant difference (\(p<0.001\)), regardless of the presence of noise (Table 3). Furthermore, the post-hoc test results showed no significant difference between the simulation and interpolated datasets (\(p>0.05\)), whereas there was a significant difference between the simulation and undersampled datasets (\(p<0.001\)), regardless of the presence of noise. Additionally, as shown in Figure 4, although the time series of the simulation, undersampled, and interpolated datasets exhibited similar trends, a detailed view of the eight peaks indicated that the peak points of the undersampled dataset were substantially different from those of the simulation dataset, especially at peaks 02, 03, 05, 06, and 08 (Figure 4).