Deep PCCT: Photon Counting Computed Tomography Deep Learning
  Applications Review
Ana Carolina Alves André Ferreira Gijs Luijten Jens Kleesiek Behrus Puladi Jan Egger Victor Alves
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.