The Use of Artificial Intelligence in Computed Tomography Image Reconstruction: A Systematic Review

Theresa Lee* and Euclid Seeram

The Use of Artificial Intelligence in Computed Tomography Image Reconstruction: A Systematic Review.

Current image reconstruction techniques in computed tomography such as filtered
back-projection and iterative reconstruction have limited use in low-dose CT imaging due to
poor image quality and reconstruction times not fit for clinical implementation.

Hence, with the increasing need for radiation dose reductions in CT, the use of artificial intelligence in
image reconstruction has been an area of growing interest.

The aim of this review is to examine the use of AI in CT image reconstruction and its effectiveness in enabling further dose reductions through improvements in image quality of low-dose CT images.

The aim of this review is to examine the use of AI in CT image reconstruction and its effectiveness in enabling further dose reductions through improvements in image quality of low-dose CT images.

This review found that deep learning-based algorithms demonstrate promising results in improving the image quality of low-dose images through noise suppression, artefact reduction, and structure preservation in addition to optimising IR methods.

In conclusion, with the two AI-based CT systems currently in clinical use showing favourable benefits, it is expected that AI algorithms will continue to proliferate and enable significant dose reductions in CT imaging.

The use of computed tomography has rapidly increased in recent decades as it allows for visualisation
of anatomical structures with high spatial and temporal resolution. However, with the extensive
amount of CT scans being performed each year, the ionising radiation inherent to CT has become a public concern.

Radiol Open J. 2020; 4(2): 30-38. doi: 10.17140/ROJ-4-129

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