# On-the-Fly Machine Learning for Improving Image Resolution in Tomography
## 4 Results
### Experimental Data  
To verify the practical applicability of our approach, we have investigated the performance of our method on experimentally acquired cone-beam CT data. **Sample** A plastic jar filled with oatmeal was scanned. The oatmeal was specifically chosen for its structure, which is consistent throughout the entire object. A package of oatmeal contains thousands  
Figure 7: 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 images in the right-most four columns are computed from the input using our proposed methods and cubic up-sampling. Magnifications of the central yellow square are displayed in the even rows. The first row displays the results on the non-blurred foam phantom reconstructions, and the third row displays the results on reconstructions of blurred foam phantom projections. The output of method B is cubically up-sampled by a factor of 4 to be the same size as the other images. of flakes, which all have roughly the same dimensions and shape, but still exhibit fine-scale features, requiring high-resolution tomography to accurately capture. Projection and reconstruction images of the oatmeal are displayed in Figure 8.  
**Data acquisition** The projection images were acquired using the custom-built and flexible CT scanner, FleX-ray Laboratory, developed by XRE NV and located at CWI [40]. The apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944 \(\times\) 1536 pixels, 14-bit, flat detector panel. The data was acquired over 360 degrees in circular motion with 2000 projections distributed evenly over the full circle. The projections were collected with 250 ms exposure time and the total scanning time was 11 minutes per acquisition. The tube voltage was 70 kV and the tube power was 42 W. This acquisition strategy was performed three times with magnification factors \(\alpha=1.09,4.38,\) and 4.38. Examples of the acquired projection images are displayed in Figure 8 and an overview of scanning time and emitted beam energy is given in Table 4. The object was centered on the detector for the first two scans, and the object was moved down to capture a region of interest above the center of the object for the final scan. These data are publicly available via [41]. **Processing** Reconstructions were computed of the full object, a central region of interest (serving as training set), and an upper region of interest (serving as test set). An example of the central slice of the oatmeal is displayed in Figure 8. The voxels in the full-object reconstruction are four times larger in each dimension than the voxels in the region-of-interest reconstructions. The training step was also performed in the same way as for the simulated data. The details are summarized in Table 2. Up-sampling the low-resolution region-of-interest slices to 1304 \(\times\) 1304 pixels takes roughly 0.56 s per slice and 13 min in total using method A with one input slice. Using method B, up-sampling a single slice takes 0.03 s, and the entire region of interest can be up-sampled in less than a minute. **Evaluation** The attenuation coefficients of the voxels in the high-resolution reconstruction are contained in the range \([-0.039,0.105]\). To enable comparison of the MSE and SSIM metrics of the experimental data with the simulated data, we rescaled the attenuation coefficients of the target volume to the range \([0,1]\), and used the same scaling factors to rescale the values of all other volumes. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Emitted Beam Energy (I)** & **Scanning Time (Minutes)** \\ \hline Full low-resolution scan & \(\eta\times\) 27,720 & 11 \\ High-resolution ROI scan & \(\eta\times\) 27,720 & 11 \\ Full high-resolution scan & \(\eta\times\) 443,520 & 176 \\ (Estimate for tiling) & & \\ \hline \hline \end{tabular}
\end{table}