# On-the-Fly Machine Learning for Improving Image Resolution in Tomography
## 4 Results
### Experimental Data

Table 4: The scanning times and emitted beam energy of the experiments. The third row contains an estimate of the duration of a full high-resolution scan with a detector tiling strategy, where a virtual large projection image is created by stitching the images for several detector positions. To achieve similar resolution as the high-resolution ROI scan, 16 detector positions would have to be stitched together. The emitted beam energy is used as a proxy for the radiation dose absorbed by the object. The efficiency of the source \(\eta\) is multiplied by the power \(P=42W\) applied to the X-ray tube, and the scanning time in seconds. Figure 8: **(a)** A projection image of the oatmeal sample; **(b)** a magnified projection image of the central ROI; **(c)** a magnified projection image of the upper ROI; **(d)** a central slice of the oatmeal sample. In addition, before calculating the SSIM, all volumes are clipped to the range \([0,1]\), similar to the simulated data. Likewise, the slices displayed in Figure 9 are rescaled and clipped to the unit range. The experimental results on the oatmeal dataset are visually compared on the central slice of the upper region of interest. This low-resolution input slice, which is displayed in Figure 9, is thus not part of the training set. The low- and high-resolution slices of the oatmeal both suffer from some noise. The high-resolution slice contains significantly more visible fine-scale features. The output of the three methods is virtually indistinguishable in the non-magnified images. In the magnified images, we see that not all fine details can be retrieved: some small features are removed and others are slightly deformed compared to the target. In the red ellipse, a large flake is visible in the low-resolution slice that appears larger than it really is due to partial volume effects. The high-resolution slice, on the other hand, shows only a small fragment of the large flake. All three networks are able to filter away a large part of the flake and retain the ridge that is visible in the high-resolution slice. The three methods significantly reduce the background noise that is present in the high-resolution target beyond what can be achieved using cubic up-sampling. Overall, all three methods sharpen features that are hard to distinguish in the input, significantly reduce the background noise, and do not introduce new features that are not present in the high-resolution slice. The quantitative results paint a similar picture, and are displayed in Table 5. The difference between the three proposed methods in terms of MSE and SSIM is small, whereas the cubic up-sampling performs worse on both metrics. Comparing method A and method B, we observe a difference between simulation and experimental data. For the pixel-limited dataset, method A performs better on the MSE and SSIM metrics, and it outputs visually more appealing and accurate results. For the real-world data, however, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Method A**} & \multicolumn{2}{c}{**Method A**} & \multicolumn{2}{c}{**Method B**} & \multicolumn{2}{c}{**Cubic up-Sampling**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Slab: 9 Slices**} & \multicolumn{2}{c}{**Slab: 1 Slice**} & \multicolumn{2}{c}{**Slab: 1 Slice**} & & \\ \hline  & MSE & SSIM & MSE & SSIM & MSE & SSIM & MSE & SSIM \\ \hline Oatmeal & 0.0027 & 0.4508 & 0.0026 & **0.4529** & **0.0025** & 0.4521 & 0.0035 & 0.4192 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Oatmeal: comparison of the MSE and SSIM between the output of the three approaches described in this section and cubic up-sampling. The best results are shown in bold for each metric. Figure 9: Oatmeal data: results of applying the trained networks to the central slice of the upper region of interest. The low-resolution slice (shown left) was not part of the training set. The high-resolution slice is the result of reconstruction (Equation (12)). The four images on the right are computed from the input using our proposed methods and cubic up-sampling.