# Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals
## 1 Introduction
Data from preliminary trials can also be valuable in providing background information useful in reducing examination time. However, the motion estimation algorithm can significantly eliminate diagnostic images' artefacts thus maximising the chance of correct diagnosis. Numerous medical imaging procedures have been struggling with one of their most prominent drawbacks, i.e., long examination times. Many algorithms for quickening of the MRI data collection have been subjects of interest for many researchers [4; 7; 8]. One of the possible scenarios of what could be developed is the change of phase encoding intervals in k-space filling [9; 10; 11; 12]. Unfortunately, this aspect usually leads to weakened image quality. Favourably, it can be overcome by applying the proposed k-space sampling pattern [8]. Single-shot echo planar imaging (SS-EPI) turned out to be one of the most frequently developed sampling schemes in DWI (Diffusion Weighted Imaging) area. However, despite its undisputed importance, some features have remained a point of confusion and reasoned subject of discourse. The presented methodology has been confronted with several state-of-art super-resolution enhancement algorithms such as Enhanced Deep Residual Networks for Single Image Super-Resolution [13], Image super-resolution using very deep residual channel attention Networks [14], as well as Residual dense network for image super-resolution exploiting the deep convolutional neural networks [15]. In order to make the comparison more reliable, the goal was to train the networks to map Low-Resolution (LR) CT and PET scans to 'ground-truth' subimages' domains. These methods relied on residual learning procedures and their improvements. They can be further adapted to take advantage of using a modified optimisation relying on removing unnecessary modules in conventional residual networks. The main idea of that algorithm is to use dynamic upsampling filters and to derive a residual image. It simplifies the motion estimation procedures by avoiding explicit motion compensation procedures. Furthermore, increasing the size of the model was recommended. This adjustment stabilises the learning step. Despite their importance in the medical image processing field, these techniques are not suitable for such highly challenging environments because of the need for reliable training sets. Moreover, with these kinds of methods, precision depends on the accuracy of image registration. Because of this, numerous Super-Resolution frameworks exploit optical flow-based procedures. It should be mentioned that public expectations regarding the medical modalities are much higher than in the case of conventional video/image enhancement. The method adapted in this paper is a severally verified globally optimal deformable registration based super-resolution algorithm [16]. One of its main constraints is spatial resolution which produces difficulties in depicting minute body structures. In the area of Computed tomography (CT), a serious concern is to reduce the radiation dose without significantly degrading the diagnostic image quality. Compressed sensing (CS) allows for the radiation dose reduction by reconstructing scans from a limited number of projections. In the technique shown in this paper, the main goal is to enhance the CT/PET hybrid scanner's image resolution as well as its quality in terms of edge delineation, keeping acquisition time at a low level, see Figures 1 and 2. In this work, the author suggests a new the CT/PET area associated technique, which blends super-resolution [7; 8] and motion correction with a robust sampling trajectory pattern. The experimental results are promising and revealed the method's true value. The main goal was to enhance image resolution as well as its quality in terms of edge delineation while keeping acquisition time at a pretty low level. Figure 1: The CT/PET inputs utilising their joint sparsity.