Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review

arXiv (Cornell University)

Abstract

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.

Authors 6

1. André Filipe Ferreira
2. Gijs Luijten

   Graz University of Technology · Artificial Intelligence in Medicine (Canada)

   Affiliation as printed

   Institute for Artificial Intelligence in Medicine , University Medicine Essen , Essen , Germany

   Institute of Computer Graphics and Vision (ICG) , Graz University of Technology , Graz , Austria

3. Jens Kleesiek

   TU Dortmund University · Artificial Intelligence in Medicine (Canada)

   Affiliation as printed

   Department of Physics , TU Dortmund University , Dortmund , Germany

   Institute for Artificial Intelligence in Medicine , University Medicine Essen , Essen , Germany

4. Behrus Puladi Aachen Department of Oral and Maxillofacial Surgery Institute of Medical Informatics

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Oral and Maxillofacial Surgery , University Hospital RWTH Aachen , Aachen , Germany

   Institute of Medical Informatics , University Hospital RWTH Aachen , Aachen , Germany

5. Jan Egger
6. Victor Alves

   University of Minho

   Affiliation as printed

   Center Algoritmi / LASI , University of Minho , Braga , Portugal