Application of Artificial Intelligence in Improving Image Quality in Radiography

Thesis Abstract

Abstract
This thesis explores the application of Artificial Intelligence (AI) in enhancing image quality within the field of Radiography. The integration of AI technology in radiography has emerged as a promising approach to address challenges related to image quality, diagnostic accuracy, and workflow efficiency. The study begins with an in-depth examination of the current landscape of radiography, highlighting the significance of image quality in diagnostic procedures. By leveraging AI algorithms and deep learning techniques, this research aims to optimize the quality of radiographic images, thereby improving diagnostic outcomes and patient care. The literature review section provides a comprehensive analysis of existing studies and technologies related to AI in radiography. Key topics covered include the principles of AI, deep learning models, image processing techniques, and their application in medical imaging. Through a critical review of relevant literature, this chapter establishes the theoretical foundation for the research, highlighting gaps in existing knowledge and opportunities for further exploration. The research methodology chapter outlines the design and implementation of the study, including data collection methods, AI algorithms employed, and evaluation metrics utilized to assess image quality improvements. The methodology also describes the process of training and testing AI models using radiographic datasets, with a focus on optimizing performance and accuracy. Findings from the study are presented in the discussion chapter, where the results of AI-enhanced image processing techniques are analyzed and compared against traditional methods. The impact of AI on image quality enhancement, artifact reduction, and diagnostic precision is evaluated through quantitative and qualitative assessments. Additionally, the implications of these findings for clinical practice and future research directions are discussed. In conclusion, this thesis summarizes the key findings and contributions of the research, emphasizing the role of AI in revolutionizing radiography practices and improving patient care. By harnessing the power of AI to enhance image quality, radiographers and healthcare professionals can make more accurate diagnoses, reduce errors, and streamline workflow processes. The study underscores the potential of AI as a transformative tool in radiography and calls for continued research and innovation in this rapidly evolving field. Overall, this thesis illuminates the transformative potential of AI in radiography, offering valuable insights into the application of advanced technologies to improve image quality and enhance diagnostic capabilities in medical imaging practices.