Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review

• DOI: 10.48550/arxiv.2402.04301
• Publication Date: 2024-02-06
• Authors: Ana Carolina Alves; André Ferreira; Gijs Luijten; Jens Kleesiek; Behrus Puladi; Jan Egger; Victor Alves
• Keywords: Photon Counting

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 a solution, addressing these issues with its innovative technology. This review delves into the recent developments applications of PCCT in pre-clinical research, emphasizing potential to overcome traditional limitations. For example demonstrated remarkable efficacy improving detection subtle abnormalities breast, providing level detail previously unattainable. Examining current literature on PCCT, it presents comprehensive analysis technology, highlighting main features scanners their varied applications. In addition, explores integration deep learning along study radiomic features, presenting successful data processing. While acknowledging advances, also discusses existing this field, paving way for future research improvements medical technologies. Despite number articles subject, due at clinical level, benefits extend various diagnostic