# CTprintNet: An Accurate and Stable Deep Unfolding Approach for Few-View CT Reconstruction
## 5 Results and Discussion
### Results on a Realistic Dataset  
As our realistic dataset, we used a set of clinical images from the AAPM Low-Dose CT Grand Challenge dataset by the Mayo Clinic ([[https://www.aapm.org/GrandChallenge/LowDoseCT/](https://www.aapm.org/GrandChallenge/LowDoseCT/)]([https://www.aapm.org/GrandChallenge/LowDoseCT/](https://www.aapm.org/GrandChallenge/LowDoseCT/)), accessed on 22 May 2023). This dataset contains images of the human abdomen with a size of \(512\times 512\) pixels obtained from full-dose acquisitions. From the entire dataset, we selected about 1500 images for the training phase and 327 for the testing phase. Experiment D. In this experiment, we trained the two architectures on data affected by noise with a standard deviation \(\sigma=0.05\) and tested them on data with noise with \(\sigma\) chosen randomly in the range \([0.04,0.08]\). Figure 11 shows the boxplots relative to the considered metrics in blue for LPP and in orange for CTprintNet. As can be seen from the boxplots, CTprintNet obtains better performances on average; in addition, it is also more stable with respect to different noise intensities. Moreover, we observe that CTprintNet boxplots are smaller with closer means and medians with respect to the LPP boxplots, confirming its superior stability. In the following, we will show and comment on three reconstructions obtained from test images with standard deviation values of 0.04, 0.065, and 0.075. We compared the reconstructions obtained from the proposed method with those obtained from the SGP algorithm where the regularization parameter was selected according to the high PSNR among 20 different tested values. The reconstructions from CTprintNet and LPP were obtained by considering the same training as before. Reconstruction D.1. This example is obtained from a test image corrupted by noise with variance \(\sigma=0.04\). Figure 12 depicts the reconstructed images and reports, for each one, the values of PSNR and SSIM. We observe that both CTprintNet and LPP effectively remove the typical artifacts of the few-view geometry and, in particular, they suppress the noise. However, in the LPP image, a white ghost detail appears. The SGP reconstruction looks blocky, and many details in the dark background are lost. Reconstruction D.2. The second example is obtained from a test image corrupted by noise with variance \(\sigma=0.065\). Figure 13 depicts the reconstructed images and reports, for each one, the values of PSNR and SSIM. The image obtained from the LPP framework presents over-smoothing and some incorrect pixels, as can be highlighted by the zoomed-in image. Hence, we can infer that CTprintNet reconstruction is advantageous over the other methods in terms of both metrics and noise reduction. Figure 10: Trends for step size \(\gamma\) (**left**), regularization parameter \(\lambda\) (**middle**), and step size barrier parameter \(\mu\) (**right**) learned from CTprintNet as the layers change. Reconstruction D.3. The last example is obtained from a test image corrupted by the highest amount of noise with a variance of \(\sigma=0.075\). Figure 14 shows that, despite the excessive noise compared to that used in the training, the proposed network is able to provide the best-quality reconstruction. In particular, the SGP image is extremely blocky, as in the previous case, and the LPP image presents again an excessive amount of smoothing and number of corrupted pixels. The results obtained from the various experiments on the realistic dataset confirmed that our architecture is stable, as already observed in Section 5.1. Figure 11: Boxplots of the PSNR (**left**) and SSIM (**right**) concerning LPP (blu line) and CTprintNet (orange line) and trained and tested on Mayo dataset. The noise variance is \(\sigma=0.05\) in the training images, and \(\sigma\) is chosen randomly in the range \([0.04,0.08]\) in the testing images. The red line indicates the median, and the black line indicates the mean value. Figure 12: Reconstruction D.1: (**a**) ground truth, (**b**) CTprintNet, (**c**) LPP, and (**d**) SGP.