CitationThe increased use of computed tomography (CT) has raised concerns regarding the radiation dose received by radiosensitive organs. It is important that practical and reliable dose reduction strategies are implemented to reduce patient radiation exposure. Aims: The purpose of this article is to evaluate the current clinical use and effectiveness of bismuth shielding as a dose reduction technique and assess its impact on image quality, in an attempt to develop a recommendation for dose reduction in CT. Methods: A systematic review of current literature was conducted using the PubMed and Scopus databases. A total of 50 relevant articles were thoroughly assessed and evaluated.Results: This review found that whilst bismuth shielding proves to provide significant dose reductions to radiosensitive organs, numerous concerns exist including wasted radiation, reduced image quality and unpredictable results when combined with AEC. Alternative methods such as tube current modulation and iterative reconstruction algorithms can provide equivalent dose savings at superior image quality, without the limitations of bismuth shields. Conclusion: Until these alternative methods become available in all departments, bismuth shielding remains a viable dose reduction strategy.
Background and purpose: The use of AI in the process of CT image reconstruction may improve image quality of resultant images and therefore facilitate low-dose CT examinations. Methods: Articles in this review were gathered from multiple databases (Google Scholar, Ovid and Monash University Library Database). A total of 17 articles regarding AI use in CT image reconstruction was reviewed, including 1 white paper from GE Healthcare. Results: DLR algorithms performed better in terms of noise reduction abilities, and image quality preservation at low doses when compared to other reconstruction techniques. Conclusion: Further research is required to discuss clinical application and diagnostic accuracy of DLR algorithms, but AI is a promising dose-reduction technique with future computational advances. R ESUM E Contexte et but : Le recours a l'intelligence artificielle (IA) dans le processus de reconstruction d'image en TDM pourrait permettre d'am eliorer la qualit e des images r esultantes et donc faciliter le4s examens de TDM a faible dose de rayonnement. M ethodologie : Les article de cette revue documentaire ont et e collig es a partir de plusieurs bases de donn ees (Google Scholar, Ovid et Monash University Library Database). Au total, 17 articles traitant de l'utilisation de l'IA en reconstruction d'images TDM ont et e examin es, incluant un livre blanc de GE Healthcare. R esultats : Les algorithmes DLR pr esentent un meilleur rendement en termes de capacit e de r eduction du bruit et de pr eservation de la qualit e de l'image a faibles doses, comparativement aux autres techniques de reconstruction. Conclusion : D'autres recherches seront n ecessaires afin de discuter des applications cliniques et de la pr ecision diagnostique des algorithmes DLR, mais l'IA constitue une technique prometteuse de r eduction de la dose avec les avances futures en mati ere de puissance informatique.
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