Charting the path forward: CT image quality assessment - an in-depth review - Siyi Xun; Qiaoyu Li; Xiaohong Liu; Pu Huang; Guangtao Zhai; Yue Sun; Peter H. N. de With; Mingxiang Wu; Tao Tan (Journal of King Saud University: Computer and Information Sciences)
Abstract Computed Tomography (CT) is a frequently utilized imaging technology that is employed in the clinical diagnosis of many disorders. However, clinical diagnosis, data storage, and management are faced with significant challenges posed by a huge volume of non-homogeneous CT data in terms of imaging quality. As a result, the quality assessment of CT images is a crucial problem that demands consideration. The history, advancements in research, and current developments in CT image quality assessment (IQA) have been examined in this paper for the first time. In this review, we collected and researched more than 500 publications related to CT-IQA published before 2024, providing visualization analysis of keywords and co-citations in the knowledge graph of these papers. This paper also discusses the research branches derived from the field of CT-IQA as well as the prospects and obstacles to its development. At present, Artificial intelligence (AI)-based CT-IQA is becoming a notable trend. It helps improve the accuracy of CT scanning equipment and the performance of CT system reconstruction algorithms, and provides a new idea for CT image processing algorithms. In the future, the rigorously validated and fully regulated AI-based CT-IQA model will become a powerful tool for medical image quality control.