Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review
<|reference_start|>Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review: Medical imaging faces challenges such as limited spatial resolution, interference from electronic noise and poor contrast-to-noise ratios. Photon Counting Computed Tomography (PCCT) has emerged as a solution, addressing these issues with its innovative technology. This review delves into the recent developments and applications of PCCT in pre-clinical research, emphasizing its potential to overcome traditional imaging limitations. For example PCCT has demonstrated remarkable efficacy in improving the detection of subtle abnormalities in breast, providing a level of detail previously unattainable. Examining the current literature on PCCT, it presents a comprehensive analysis of the technology, highlighting the main features of scanners and their varied applications. In addition, it explores the integration of deep learning into PCCT, along with the study of radiomic features, presenting successful applications in data processing. While acknowledging these advances, it also discusses the existing challenges in this field, paving the way for future research and improvements in medical imaging technologies. Despite the limited number of articles on this subject, due to the recent integration of PCCT at a clinical level, its potential benefits extend to various diagnostic applications.<|reference_end|>
@article{alves2024deep,
    title={Deep PCCT: Photon Counting Computed Tomography Deep Learning
  Applications Review},
    author={Ana Carolina Alves, Andr'e Ferreira, Gijs Luijten, Jens Kleesiek,
  Behrus Puladi, Jan Egger, Victor Alves},
    journal={arXiv preprint arXiv:2402.04301},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04301},
    primaryClass={eess.IV cs.CE cs.CV cs.LG}
}