Photon-counting x-ray computed tomography is the latest advancement in the field of medical x-ray imaging, and this new technology is now becoming available to healthcare institutions. Our research is centered around a silicon-based photon-counting CT scanner prototype based on a detector developed by our research group, which promises high spatial resolution, improved noise performance and better energy resolution compared to what is available today. In order to produce the best possible image based on the data that can be acquired with this prototype scanner, it is necessary to simulations of data acquisition with this new scanner studies and use this simulated data to understand the factors affecting image quality and developing improved image reconstruction methods. The purpose of this project is therefore to simulate data acquisition with a simulation model of this photon-counting scanner using CATSim, a software package developed by General Electric Company for simulating x-ray computed tomography imaging. This will result in simulated projection data, and this data will be used for two main purposes. The first is to evaluate the image quality achievable with this new detector technology, by calculating contrast-to-noise ratio and more sophisticated metrics of image quality both in the raw data and in the reconstructed image. The benefit of doing this in simulations, as a complement to measurements, is that this provides a greater degree of flexibility in terms of varying parameters or inserting simulated features of known composition in the imaged object. The second main purpose is to generate simulated training data for deep-learning image reconstruction. By generating thousands of simulated images, based on models of human anatomy or on actual patient scans, we can generate input and label images for training deep neural networks for a variety of image enhancement tasks, such as denoising and artifact correction. In this regard, the present project, which will use CPU-based resources, will serve as a complement to two other GPU-based allocations, NAISS 2023/22-85 Highly accurate photon-counting CT for cancer imaging with deep-learning image reconstruction and Berzelius-2023-103 (Deep-learning data processing for photon-counting CT), wherein these simulated images will be used to train deep neural networks. In this way, the CPU- and GPU-based compute allocations can serve an important role in developing future medical x-ray imaging equipment and ensuring that the capabilities of the new hardware can be translated into substantial benefit for the patients.