Abstract
The purpose of this work is to improve the quality of medical services, to acquire financial benefits for hospitals, and to achieve beneficial social consequences. These are the goals and the motivations that are driving this work. Using the framework of deep learning, it studies the composition of medical linear accelerators, as well as their working principle, acceptance, and the routine quality assurance (QA) and quality control (QC) procedures. Additionally, it examines the acceptability of these applications. This article focuses on the acceptance items and detection procedures of accelerators, as well as common technological failures and solutions. In addition to providing a summary of emergency maintenance experience and analyzing the usual technical failures of medical electronic linear accelerators, it also provides an analysis of the common technical failures and solutions. Given that Chinese radiation institutions are already utilizing medical accelerators, the authors of this study analyze the relevant development state of medical electron linear accelerators as well as the future development trends of these accelerators. This is done in light of the fact that medical accelerators are currently being utilized by Chinese radiation institutes today. It provides further explanation on the unified implementation and refinement of its usage standards, in addition to providing information regarding conventional quality assurance and quality control. The findings provide a theoretical basis as well as a practical significance for the continued development of linear accelerators for use in medical applications in the future.
Keywords: Deep Learning, Emergency Maintenance, Medical Linear Accelerator, Quality Control.