# Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals
## 7 Results  
In the experimental studies, several CT, PET, and CT/PET in vivo and phantom images have been processed using different reconstruction algorithms. The term 'phantom' refers to a specially designed object that is scanned or imaged in the field of medical modalities to evaluate, analyse, and tune the performance of various imaging devices. The employed realistic phantom produces slices/images exploiting some breathing surface illustrating possible inhalation and exhalation-based disfigurements. Moreover, these models can cover any arbitrary motion tumours' motion trajectories. The internal organs' motion parameters are imposed by quadratic polynomials. The second parts of the experimental studies were conducted on noisy, blurred, motion artefacts affected Shepp-Logan and Zubal phantoms. In the experiment, various number of projection lines and Fourier coefficient were tested. The in-plane motion performed on the phantoms during the acquisition process was a simulated affine motion model. Additionally, some elements of images have also been warped according to random values defined inside. Additionally, to illustrate possible internal organ tissue blur, their motion artefacts were modelled using simulated locally affine transformations. Then, the low-resolution images have been blurred using a Gaussian blur kernel. In comparison, different reconstruction methods under different circumstances were analysed. Figures 3-9 clearly proved the superiority of the super-resolution image reconstruction. The fusion of the Adaptive Tomography Acquisition with SRR allowed improved image quality while maintaining reasonable acquisition times. It is worth underlining that applying Adaptive Tomography Acquisition (ATA) leads to data acquisition acceleration when compared to the different methods. To analyse the presented super-resolution algorithm, various types of images were examined. The first sets of images were standard test images. The high-resolution images have been artificially degraded by predetermined factors. In this way, the resulting LR set of images contains local geometric deformations. Later, framed images, prepared this way, were blurred, noised, and decimated. All the trials were performed in MathWorks Matlab (Natick, MA, USA). The expectations related to the method's performance are high, mostly due to its potential advantages, i.e., obtaining high contrast while keeping a great resolution. This work deals with combining CT/PET with compressed sensing and super-resolution. It can be clearly seen that the obtained improvements have led to much better sharpness, edge interpretations, and contrast. The compressively sensed Super-Resolution images have achieved the PSNR. The statistical studies were conducted using a \(t\)-student test to verify the hypothesis that there was no significant difference in ratings for image quality between super-resolution using sparse input and fully sampled ground truth images. The proposed method's output images that had been chosen more often turned out to have significantly higher image quality metric values (assessed and confirmed by a radiologist), and better delineation of anatomic structures' details than the images obtained using competitive image enhancement algorithms. The disparities in PSNRs were also assessed utilising an exact Bowker symmetry test. The accomplishments of motion correction were estimated by calculating the difference between images with motion and the referencing images (without a motion distortion added) for each subject. The consequence of shifts between data obtained with and without artificially added motion was also estimated using paired \(t\)-tests. All the conducted statistical tests exposed that the improvement is statistically significant. It is clearly detailed (see Tables 1-10) that the compression ratios highly affect the PSNR values. Note that the motion correction algorithm affected the achieved metrics' values. The algorithm has been tested with the use of CT/PET output data sets. Thus, two unique sets were obtained.