Introduction: Low-dose computed tomography tends to produce lower image quality than normal dose computed tomography (CT) although it can help to reduce radiation hazards of CT scanning. Research has shown that Artificial Intelligence (AI) technologies, especially deep learning can help enhance the image quality of low-dose CT by denoising images. This scoping review aims to create an overview on how AI technologies, especially deep learning, can be used in dose optimisation for low-dose CT. Methods: Literature searches of ProQuest, PubMed, Cinahl, ScienceDirect, EbscoHost Ebook Collection and Ovid were carried out to find research articles published between the years 2015 and 2020. In addition, manual search was conducted in SweMedþ, SwePub, NORA, Taylor & Francis Online and Medic. Results: Following a systematic search process, the review comprised of 16 articles. Articles were organised according to the effects of the deep learning networks, e.g. image noise reduction, image restoration. Deep learning can be used in multiple ways to facilitate dose optimisation in low-dose CT. Most articles discuss image noise reduction in low-dose CT. Conclusion: Deep learning can be used in the optimisation of patients' radiation dose. Nevertheless, the image quality is normally lower in low-dose CT (LDCT) than in regular-dose CT scans because of smaller radiation doses. With the help of deep learning, the image quality can be improved to equate the regulardose computed tomography image quality. Implications to practice: Lower dose may decrease patients' radiation risk but may affect the image quality of CT scans. Artificial intelligence technologies can be used to improve image quality in low-dose CT scans. Radiologists and radiographers should have proper education and knowledge about the techniques used.
Introduction:The purpose of the study is to clarify the domain of radiography science. The main goal of science is building knowledge and developing ideas and theories that explain, predict, understand or interpret the phenomena investigated. Each discipline has its own perspective to view and study the phenomena of interest. The disciplinary perspective enables researchers in radiography science to reason and conceptualize phenomena, but it can also restrict them. The aim of this review was to investigate phenomena that are at the core of the discipline of radiography science. Methods: This study used a scoping review as the method. A systematic search was carried out in the databases: Science Direct, Pubmed, Cinahl, and Scopus. The selection of articles was conducted by predetermined inclusion and exclusion criteria for the title, abstract and full text. After the exclusion process, fourteen articles were selected for a final review. The articles were analyzed with inductive content analysis. Results: From the articles, 117 research interests were identified; these were merged into 17 categories and further into six main categories. The main categories represent the phenomena radiography science investigates. The phenomena are: the radiographers' profession, clinical practices in diagnostic and therapeutic patient pathways, safe and high quality use of radiation, radiographic technology, discipline, management and leadership of radiography professionals Conclusions: Radiography science has a conceptual structure of its own that needs more investigation. Radiography science researches distinctive phenomena and specialized knowledge, common to researchers from different traditions and subspecialties thus justifying its existence. Implications for practice: Investigating the core phenomena of interest in radiography science can support researchers in the field to focus their research and to develop the concepts of radiography.
Radiography science is a new discipline among health sciences. It is a discipline that investigates phenomena in medical imaging, radiation therapy, and nuclear medicine. It has merged from the need to provide research evidence to support these services. The domain of the discipline needs clarification and more research should be focused on its paradigmatic issues. Radiography research priorities have been previously charted on a national level in different countries but the viewpoint has been that of the needs of the profession, not of the discipline. This study aimed to identify the priorities of the discipline. The method chosen was a modified version of the Delphi technique with two rounds. The expert panel consisted of 24 European radiography researchers with long professional experience. This study shows that the research priorities in radiography science are related to the phenomena of radiographers' profession, clinical practices, and the safe and high‐quality use of radiation and technology for medical imaging, radiotherapy, and nuclear medicine. Identifying these priorities can help focus research onto most important topics and clarify disciplinary perspective.
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